Self interference noise cancellation to support multiple frequency bands with neural networks or recurrent neural networks

ABSTRACT

Examples described herein include systems and methods which include wireless devices and systems with examples of multiple frequency bands transmission with a recurrent neural network that compensates for the self-interference noise generated by power amplifiers at harmonic frequencies of a respective wireless receiver. The recurrent neural network may be coupled to antennas of a wireless device and configured to generate the adjusted signals that compensate self-interference. The recurrent neural network may include a network of processing elements configured to combine transmission signals into sets of intermediate results. Each set of intermediate results may be summed in the recurrent neural network to generate a corresponding adjusted signal. The adjusted signal is receivable by a corresponding wireless receiver to compensate for the self-interference noise generated by a wireless transmitter transmitting on the same or different frequency band as the wireless receiver is receiving.

BACKGROUND

There is a need for wireless communication systems to support “fifthgeneration” (5G) systems, with some wireless communication systemsalready implementing specific 5G protocols (e.g., a protocol to operateat 3.5 GHz). Such 5G systems may be implemented using multiple-inputmultiple-output (MIMO) techniques, including “massive MIMO” techniques,in which multiple antennas (more than a certain number, such as 8 in thecase of example MIMO systems) are utilized for transmission and/orreceipt of wireless communication signals.

Moreover, machine learning (ML) and artificial intelligence (AI)techniques are in need of higher-capacity and widely-connectedinfrastructures to train devices that use such techniques. For example,machine learning is a type of AI that uses data sets, often a largevolume of data, to train machines on statistical methods of analysis.And there is need for higher-capacity memory and multichip packages tofacilitate AI training and inference engines, whether in the cloud orembedded in mobile and edge devices. For example, large volumes of dataare needed in real-time to train AI systems and accelerate inference.Additionally, there is a need for communication capacity at mobile orsensor devices at the edge network, for example, to facilitateprocessing of data acquired at the edge network, e.g., to efficientlyoffload such data to a data center for AI or ML techniques to beapplied.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a system arranged in accordancewith examples described herein.

FIG. 2A is a schematic illustration of an electronic device arranged inaccordance with examples described herein.

FIG. 2B is a schematic illustration of an electronic device arranged inaccordance with examples described herein.

FIG. 3 is a schematic illustration of a wireless transmitter.

FIG. 4 is a schematic illustration of wireless receiver.

FIG. 5A is a schematic illustration of an example neural network inaccordance with examples described herein.

FIG. 5B is a schematic illustration of a recurrent neural networkarranged in accordance with examples described herein.

FIGS. 5C-5E are schematic illustrations of example recurrent neuralnetworks in accordance with examples described herein.

FIG. 6A is a schematic illustration of an electronic device arranged inaccordance with examples described herein.

FIG. 6B is a schematic illustration of an electronic device arranged inaccordance with examples described herein.

FIG. 7A is a schematic illustration of a full duplex compensation methodin accordance with examples described herein.

FIG. 7B is a flowchart of a method in accordance with examples describedherein.

FIG. 8 is a block diagram of a computing device arranged in accordancewith examples described herein.

FIG. 9 is a schematic illustration of a wireless communications systemarranged in accordance with aspects of the present disclosure.

FIG. 10 is a schematic illustration of a wireless communications systemarranged in accordance with aspects of the present disclosure.

FIG. 11 is a schematic illustration of a wireless communications systemarranged in accordance with aspects of the present disclosure.

FIG. 12 is a schematic illustration of a communications system arrangedin accordance with aspects of the present disclosure.

DETAILED DESCRIPTION

Full duplex communication may be desirable for a variety of devices.Full duplex communication generally may refer to an ability to both sendand receive transmissions, in some cases simultaneously and/or partiallysimultaneously. In examples of systems employing full duplexcommunication, it may be desirable to cancel the interference generatedby antennas or nonlinear power amplifiers in the system, e.g.,self-interference. Moreover, full duplex communication may be desirableon devices that employ multiple frequency bands, including separatefrequency bands for different communication protocols.

Different communication protocols may exist for varying generations ofwireless devices. For example, a wireless device may include atransceiver system for 5G wireless communications intended to transmitand receive at 3.5 GHz (e.g., referred to as the New Radio (NR) Band),and another transceiver system for 4G wireless communications intendedto transmit and receive at 1.8 GHz (e.g., referred to as the Long-TermEvolution (LTE) band). In some implementations, such transceiver systemsthat operate wholly on a specific generational system may be referred toas a standalone system. For example, a 5G wireless system, includingvarious 5G devices such as 5G Internet of Things (“IoT”) wireless sensordevices, to communicate data to a data center may be referred to as a 5Gstandalone system. Such a 5G standalone system may still experienceeffects of interference, for example, from a 4G standalone system thatoperates on a different frequency band, which may generate interferingfrequencies that interfere with communicated signals transmitted orreceived via the 5G standalone system, e.g., when communicating data tothe data center. Accordingly, a wireless device that may communicateusing either the 4G or 5G bands may create self-interference on one ofthe bands. Accordingly, there is a need to compensate for suchself-interference in an efficient and timely manner such that astandalone system may operate on the wireless device in a network thatmay experience the effects of interference from a different standalonesystem of another wireless device or another standalone system on thewireless device itself. As described herein, a recurrent neural networkmay be used compensate for such interference using, in part,higher-order memory effects that model the effects of leading andlagging envelopes of self-interference signals.

Moreover, such 5G standalone systems may be preferable to operate in aremote setting, e.g., not near a city with a wireless metropolitanaccess network (MAN)). Such 5G systems can operate over greaterdistances (e.g., 1 km, 5 km, 50 km, 500 km, or 5000 km); in contrast toa metropolitan-geographic area, which may be restricted to smallerdistances (e.g., 10 m, 100 m, 1 km, or 5 km). Accordingly, a 5Gtransceiver system may need to communicate long distances in anenvironment with various degrading environmental effects. Therefore, the5G systems and devices described herein can communicate data in wirelessenvironments that experience effects of weather conditions over greatdistances and/or other environmental effects to the wirelessenvironment.

On top of the challenges of environmental effects and distance, thetransceiver system may experience interference. For example, in contrastto a conventional wireless MAN system that may have a line-of-sight(LOS) with a wireless subscriber, a 5G wireless system may include adata center communicating with a remote agricultural device that isexperiencing cloudy weather in a temperate environment (e.g., the PugetSound region). As such, the remote agricultural device may not have adirect LOS with the data center because the LOS is occluded by clouds orother environmental factors. In such a case, examples described hereinmay compensate for interference generated by other antennas or nonlinearpower amplifiers co-located on the same physical device or system; aswell as compensating for the environmental effects that a 5Gcommunication signal may experience due to its communication path over agreater distance than that of a wireless MAN.

Examples described herein may compensate for interference generated byother antennas co-located on the same physical device or system (e.g.,interference created by an antenna on a MIMO device). For example, atransmitting antenna may generate interference for nearby receivingantennas, including one or more antennas which may be co-located on asame physical device or system. The transmitting antenna may generateenergy at the transmitting frequency and also at harmonics of thetransmitting frequency. Accordingly, receiving systems sensitive to thetransmitting frequency or harmonics of the transmitting frequency may beparticularly susceptible to interference from the transmitting antennain some examples.

Moreover, nonlinear power amplifiers, which are frequently employed intransmitters and/or transceivers of wireless communication systems, maycontribute to creation of interference at harmonics of the transmittingfrequency. For example, a nonlinear power amplifier may create poweramplifier noise that interferes with a frequency band that is twice orthree times the frequency to be amplified (e.g., the transmittingfrequency). Multiples of the frequency to be amplified may be referredto as harmonic frequencies. Accordingly, a frequency that is twice thefrequency to be amplified may be referred to as a second-order harmonic(2f₀); and a frequency that is thrice the frequency to be amplified, athird-order harmonic (3f₀), where f₀ is the frequency to be amplified.Such harmonic frequency components may be introduced into transmittedsignals by the power amplifier, which may generate energy at theharmonic frequencies due in part to the nonlinear characteristics of thepower amplifier.

Such nonlinear characteristics of the power amplifier may also introduceother nonlinear components into transmitted signals, for example, ifmore than one frequency is involved in the data signal (e.g., a datasignal to be transmitted) when provided to the power amplifier. Forexample, if an additional frequency f₁ is also to be amplified inconjunction with f₀, additional frequency components may be introducedby power amplifier noise into the transmitted signals at varyingfrequencies representing combinations of the frequencies to be amplifiedand/or their harmonics, such as f₀−f₁, 2f₀−f₁, and 3f₀−f₁. For example,in a mathematical representation, nonlinear characteristics oradditional frequency components may be incorporated into a model ofpower amplifier behavior as harmonic components added into the amplifiedresponse of a data signal at a particular frequency, with the harmoniccomponents and additional frequency components being related to thatparticular frequency.

In the example of full duplexing (FD), an antenna transmitting atransmission on a certain frequency band may create interference for anearby antenna (e.g., an antenna co-located on the same device), whichmay be intended to receive a transmission on a different frequency band.Such interference may be referred to as self-interference.Self-interference may disrupt the accuracy of signals transmitted orreceived by the MIMO device. Examples described herein may compensatefor self-interference at an electronic device, which may aid inachieving full duplex transmission, thereby also achievinghigher-capacity for a wireless network, e.g., a 5G wireless network. Anetwork of processing elements may be used to generate adjusted signalsto compensate for self-interference generated by the antennas of theelectronic device.

5G systems may advantageously make improved usage of additionalfrequency bands, for example, to improve spectrum efficiency. Frequencybands in some systems may be assigned by regulatory authorities such asthe Federal Communication Commission (FCC). Assignments may be made, forexample, according to different applications such as digitalbroadcasting and wireless communication. These licensed and assignedfrequencies may be inefficiently used if there is simply time-divisionduplex (TDD), frequency-division duplex (FDD) or half-duplex FDD mode,which are duplexing modes often used in existing wireless applications.Such modes may not be acceptable when improved efficiency is demandedfrom the wireless spectrum.

Moreover, with the fast development of digital transmission andcommunications, there are fewer and fewer unlicensed frequency bands,and it may be advantageous to use those licensed frequency bands in afull duplex transmission mode (e.g., transmitting and receiving onmultiple frequency bands). For example, the FCC has officially proposedto open a frequency range around or about 3.5 GHz. Moreover, some 5Gstandards specify that such new frequency bands are to be utilized inconjunction with existing frequency bands (e.g., 4G frequency bands).Examples described herein may be utilized to achieve full duplextransmission in some examples on multiple frequency bands including theaforementioned frequency ranges of the 4G and 5G standalone systems. Insome examples described herein, a wireless device or system may transmitand receive on this new narrowband frequency range, while alsotransmitting and receiving on other frequency bands, such as legacyfrequency bands at 4G frequency bands (e.g., 1.8 GHz) or other 5Gfrequency bands. Full-duplex (FD) transmission may allow such a wirelesscommunication system to transmit and receive the signals, at leastpartially simultaneously, on different frequency bands. This may allowFD 5G systems to interoperate with other frequency bands.

Examples described herein include systems and methods which includewireless devices and systems with a recurrent neural network. Therecurrent neural network may utilize a network of processing elements togenerate a corresponding adjusted signal for self-interference that anantenna of the wireless device or system is expected to experience dueto signals to be transmitted by another antenna of the wireless deviceor system. Such a network of processing elements may combinetransmission signals to provide intermediate processing results that arefurther combined, based on respective weights, to generate adjustedsignals. The network of processing elements may be referred to as aneural network. In some implementations with delayed versions ofintermediate processing results being utilized, such a network ofprocessing elements may be referred to as a recurrent neural network. Arespective weight vector applied to the intermediate processing resultmay be based on an amount of interference expected for the respectivetransmission signal from the corresponding intermediate processingresult.

In some examples, a recurrent neural network may include bitmanipulation units, multiplication/accumulation (MAC) processing units,and/or memory look-up (MLU) units. For example, layers of MAC processingunits may weight the intermediate processing results using a pluralityof coefficients (e.g., weights) based on a minimized error for the allor some of the adjustment signals that may generated by a recurrentneural network. In minimizing the error for the adjustment signals, awireless device or system may achieve full duplex transmission utilizingthe recurrent neural network.

Examples described herein additionally include systems and methods whichinclude wireless devices and systems with examples of mixing input datawith such coefficient data in multiple layers ofmultiplication/accumulation units (MAC units) and corresponding memorylook-up units (MLUs). For example, a number of layers of MAC units maycorrespond to a number of wireless channels, such as a number ofchannels received at respective antennas of a plurality of antennas. Inaddition, a number of MAC units and MLUs utilized is associated with thenumber of channels. For example, a second layer of MAC units and MLUsmay include m−1 MAC units and MLUs, where m represents the number ofantennas, each antenna receiving a portion of input data.Advantageously, in utilizing such a hardware framework, the processingcapability of generated output data may be maintained while reducing anumber of MAC units and MLUs, which are utilized for such processing inan electronic device. In some examples, however, where board space maybe available, a hardware framework may be utilized that includes m MACunits and m MLUs in each layer, where m represents the number ofantennas.

Multi-layer neural networks (NNs) and/or multi-layer recurrent neuralnetworks (RNNs) may be used to transmit wireless input data (e.g., aswireless input data to be transmitted via an antenna). The NNs and/orRNNs may have nonlinear mapping and distributed processing capabilitieswhich may be advantageous in many wireless systems, such as thoseinvolved in processing wireless input data having time-varying wirelesschannels (e.g., autonomous vehicular networks, drone networks, orInternet-of-Things (IoT) networks). In this manner, neural networksand/or recurrent neural networks described herein may be used togenerate adjusted signals to compensate for self-interference generatedby the power amplifiers of the electronic device, thereby facilitatingfull duplex communication for various wireless protocols (e.g., 5Gwireless protocols).

In cancelling for self-interference using RNNs, wireless systems anddevices described herein may increase capacity of their respectivecommunication networks, with such systems being more invariant to noisethan traditional wireless systems that do not use RNNs (e.g., utilizingtime-delayed versions of processing results). For example, the recurrentneural networks may be used to reduce self-interference noise that willbe present in transmitted signals (e.g., amplified signals from a poweramplifier of a wireless transceiver) based partly on the signals to betransmitted, e.g., as output from a power amplifier to an antenna fortransmission. Using time-delayed versions of processing results in anRNN of amplified signals, the self-interference noise created by poweramplifiers may be compensated, as the RNN utilizes respective poweramplifier outputs with respect to the time-delayed versions of the inputdata (e.g., power amplifier output data). In this manner, recurrentneural networks may be used to reduce and/or improve errors which may beintroduced by such self-interference noise. Advantageously, with such animplementation, wireless systems and devices implementing such RNNsincrease capacity of their respective wireless networks becauseadditional data may be transmitted in such networks, which would nototherwise be transmitted due to the effects of self-interference noise,e.g., which limits the amount of data to be transmitted due tocompensation schemes in traditional wireless systems.

FIG. 1 is a schematic illustration of a system arranged in accordancewith examples described herein. System 100 includes electronic device102, electronic device 110, antenna 101, antenna 103, antenna 105,antenna 107, antenna 121, antenna 123, antenna 125, antenna 127,wireless transmitter 131, wireless transmitter 133, wireless receiver135 and, wireless receiver 137. Antennas 101, 103, 105, 107, 121, 123,125, and 127 may be dynamically tuned to different frequencies or bands,in some examples. The electronic device 102 may include antenna 121associated with a first frequency, antenna 123 associated with a secondfrequency, antenna 125 associated with the first frequency, antenna 127associated with the second frequency, wireless transmitter 131 for thefirst frequency, wireless transmitter 133 for the second frequency,wireless receiver 135 for the first frequency, and wireless receiver 137the second frequency. The electronic device 110 may include the antenna101 associated with the first frequency, antenna 103 associated with thesecond frequency, antenna 105 associated with the first frequency,antenna 107 associated with the second frequency, wireless transmitter111 for the first frequency, wireless transmitter 113 for the secondfrequency, wireless receiver 115 for the first frequency, and wirelessreceiver 117 the second frequency.

In operation, electronic devices 102, 110 can operate in a full duplextransmission mode between the respective antennas of each electronicdevice. In an example of a full duplex transmission mode, on a firstfrequency band, wireless transmitter 131 coupled to antenna 121 maytransmit to antenna 105 coupled to wireless receiver 115; while, at thesame time or during at least a portion of a common time period, on asecond frequency band, wireless transmitter 113 coupled to antenna 103may transmit to antenna 127 coupled to wireless receiver 137, in someexamples. Self-interference received by antenna 127 or antenna 105 fromthe respective transmissions at antenna 121 and antenna 103 may be atleast partially compensated by the systems and methods described herein.Self-interference may generally refer to any wireless interferencegenerated by transmissions from antennas of an electronic device tosignals received by other antennas, or same antennas, on that sameelectronic device.

The electronic device 102 can receive self-interference noise associatedwith the first frequency from antenna 121 on a wireless path from theantenna 121 to the antenna 127. The self-interference noise received atthe antenna 127 may be interference generated at frequencies based onthe first frequency transmitted by the antenna 121 and/or one or moreharmonics of the first frequency transmitted by the antenna 121.Similarly, the electronic device 110 can receive self-interference noiseat the antenna 107 associated with the second frequency from antenna 103on a wireless path from the antenna 103 to the antenna 107. Theself-interference noise received at the antenna 107 may be interferencegenerated by frequencies based on the same, second frequency transmittedby the antenna 103. While the antennas 127 and 107 may not be receivingwireless transmission from electronic devices 102, 110 in this example,the antennas 127, 107 may be receiving wireless transmission signalsfrom other electronic devices in system 100, such that theself-interference noise received at antennas 127, 107 may degrade thereception of such signals. With the systems and methods describedherein, such self-interference noise may be compensated for so that therespective wireless receivers 137, 117 may experience improved abilityto receive their desired signals.

In some examples of the full duplex transmission mode, on a firstfrequency band, wireless transmitter 131 coupled to antenna 121 maytransmit to antenna 105 coupled to wireless receiver 115, while, at thesame time or during at least a portion of the same time, on a secondfrequency band, wireless transmitter 133 coupled to antenna 123 maytransmit to antenna 107 coupled to wireless receiver 117, in someexamples. Antenna 127 may accordingly have incident energy fromtransmissions from the antenna 121 at the first frequency and relatedfrequencies (e.g., harmonics) and incident energy from transmissionsfrom the antenna 123 at the second frequency and related frequencies(e.g., harmonics). The incident energy from the antennas 121 and 123 mayin some examples be sufficiently close to the intended receive frequencyat the antenna 127 that they interfere with transmissions intended to bereceived by the antenna 127. Similarly, antenna 125 may have incidentenergy at the first frequency and related frequencies from the antenna121 and at the second frequency and related frequencies from the antenna123.

However, in some examples, the energy at least second frequency andrelated frequencies from the antenna 123 may not be sufficiently closeto (e.g., within the sensitivity of the receiver) the intended receivefrequency of the antenna 125. Note also that the antennas 127 and 125may be, at least partially simultaneously during transmission of signalsfrom antennas 121 and 123, receiving wireless transmissions fromelectronic device 110 or another electronic device in system 100, suchthat the energy from other antennas incident at antennas 127, 125 maydegrade the reception of such signals. With the systems and methodsdescribed herein, such self-interference noise may be at least partiallycompensated so that the respective wireless receivers 137, 135 may haveimproved reception of the intended transmissions.

Electronic devices described herein, such as electronic device 102 andelectronic device 110 shown in FIG. 1 may be implemented using generallyany electronic device for which communication capability is desired. Forexample, electronic device 102 and/or electronic device 110 may beimplemented using a mobile phone, smartwatch, computer (e.g. server,laptop, tablet, desktop), or radio. In some examples, the electronicdevice 102 and/or electronic device 110 may be incorporated into and/orin communication with other apparatuses for which communicationcapability is desired, such as but not limited to, a wearable device, amedical device, an automobile, airplane, helicopter, appliance, tag,camera, or other device.

While not explicitly shown in FIG. 1, electronic device 102 and/orelectronic device 110 may include any of a variety of components in someexamples, including, but not limited to, memory, input/output devices,circuitry, processing units (e.g. processing elements and/orprocessors), or combinations thereof. For example, electronic device 102or electronic device 110 may each implement one or more processing unitsdescribed herein, such as a recurrent neural network 512 with referenceto FIGS. 5C-5E, or any combinations thereof.

The electronic device 102 and the electronic device 110 may each includemultiple antennas. For example, the electronic device 102 and electronicdevice 110 may each have more than two antennas. Three antennas each areshown in FIG. 1, but generally any number of antennas may be usedincluding 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 32, or 64antennas. Other numbers of antennas may be used in other examples. Insome examples, the electronic device 102 and electronic device 110 mayhave a same number of antennas, as shown in FIG. 1. In other examples,the electronic device 102 and electronic device 110 may have differentnumbers of antennas.

Generally, systems described herein may include multiple-input,multiple-output (“MIMO”) systems. MIMO systems generally refer tosystems including one or more electronic devices which transmittransmissions using multiple antennas and one or more electronic deviceswhich receive transmissions using multiple antennas. In some examples,electronic devices may both transmit and receive transmissions usingmultiple antennas. Some example systems described herein may be “massiveMIMO” systems. Generally, massive MIMO systems refer to systemsemploying greater than a certain number (e.g. 8) antennas to transmitand/or receive transmissions. As the number of antennas increase, sogenerally does the complexity involved in accurately transmitting and/orreceiving transmissions.

Although two electronic devices (e.g. electronic device 102 andelectronic device 110) are shown in FIG. 1, generally the system 100 mayinclude any number of electronic devices.

Electronic devices described herein may include receivers, transmitters,and/or transceivers. For example, the electronic device 102 of FIG. 1includes wireless transmitter 131 and wireless receiver 135, and theelectronic device 110 includes wireless transmitter 111 and wirelessreceiver 115. Generally, receivers may be provided for receivingtransmissions from one or more connected antennas, transmitters may beprovided for transmitting transmissions from one or more connectedantennas, and transceivers may be provided for receiving andtransmitting transmissions from one or more connected antennas. Whileboth electronic devices 102, 110 are depicted in FIG. 1 with individualwireless transmitter and individual wireless receivers, it can beappreciated that a wireless transceiver may be coupled to antennas ofthe electronic device and operate as either a wireless transmitter orwireless receiver, to receive and transmit transmissions. For example, atransceiver of electronic device 102 may be used to providetransmissions to and/or receive transmissions from antennas 121 and 123,while other transceivers of electronic device 110 may be used to providetransmissions to and/or receive transmissions from antenna 101 andantenna 103.

Generally, multiple receivers, transmitters, and/or transceivers may beprovided in an electronic device—one in communication with each of theantennas of the electronic device. The transmissions may be inaccordance with any of a variety of protocols, including, but notlimited to 5G signals, and/or a variety of modulation/demodulationschemes may be used, including, but not limited to: orthogonal frequencydivision multiplexing (OFDM), filter bank multi-carrier (FBMC), thegeneralized frequency division multiplexing (GFDM), universal filteredmulti-carrier (UFMC) transmission, bi orthogonal frequency divisionmultiplexing (BFDM), sparse code multiple access (SCMA), non-orthogonalmultiple access (NOMA), multi-user shared access (MUSA) andfaster-than-Nyquist (FTN) signaling with time-frequency packing. In someexamples, the transmissions may be sent, received, or both, inaccordance with 5G protocols and/or standards.

Examples of transmitters, receivers, and/or transceivers describedherein, such as the wireless transmitter 131, wireless transmitter 133,wireless receiver 115, or wireless receiver 117, may be implementedusing a variety of components, including, hardware, software, firmware,or combinations thereof. For example, transceivers, transmitters, orreceivers may include circuitry and/or one or more processing units(e.g. processors) and memory encoded with executable instructions forcausing the transceiver to perform one or more functions describedherein (e.g. software).

FIG. 2A is a schematic illustration 200 of an electronic device 110arranged in accordance with examples described herein. The electronicdevice 110 may also include recurrent neural network 240, compensationcomponent 245, and compensation component 247. Each wireless transmitter111, 113 may be in communication with a respective antenna, such asantenna 101, antenna 103 via respective power amplifiers, such as poweramplifiers 219, 229. Each wireless transmitter 111, 113 receives arespective data signal, such as data signals 211, 213. The wirelesstransmitters 111, 113 may process the data signals 211, 213 with theoperations of a radio-frequency (RF) front-end and in conjunction withthe power amplifiers 219, 229 generate amplified signals x₁(n), x₂(n)221, 223. Recurrent neural network 240 and power amplifiers 219, 229 maybe in communication with one another, e.g., to receive the generatedamplified signals x₁(n), x₂(n) 221, 223.

The amplified data signals x₁(n) 221 and x₂(n) 223 are provided in theelectronic device 110 to the recurrent neural network 240. For example,the amplified data signals x₁(n), x₂(n) 221, 223 may be provided to therecurrent neural network 240 via an internal path from an output of arespective power amplifier 219, 229. Accordingly, output paths of thewireless transmitters 111, 113 and the recurrent neural network 240 maybe in communication with one another. The recurrent neural network 240,therefore, receives a first amplified data signal x₁(n) 221 associatedwith a first frequency from the wireless transmitter 111 for the firstfrequency and the power amplifier 219; and, a second amplified datasignal x₂(n) 223 associated with the second frequency from the wirelesstransmitter 113 for the second frequency and the power amplifier 229.

Recurrent neural network 240 and compensation components 245, 247 may bein communication with one another. Each wireless receiver may be incommunication with a respective antenna, such as antenna 105, 107 via arespective compensation component, such as compensation component 245,247, and respective low-noise amplifiers (LNA) 249, 259. In someexamples, a wireless transmission received at antennas 105, 107 may becommunicated to wireless receiver 115, 117 after compensation ofself-interference by the respective compensation component 245, 247 andamplification of the compensated, received signal by LNAs 249, 259. Eachwireless receiver 115, 117 processes the received compensated, andamplified wireless transmission to produce a respective received datasignal, such as received data signals 255, 257. In other examples,fewer, additional, and/or different components may be provided.

Examples of recurrent neural networks described herein may generate andprovide adjusted signals to compensation components. So, for example,the recurrent neural network 240 may generate adjusted signals y₁(n) 241and y₂(n) 243 and provide such adjusted signals to the compensationcomponents 245, 247. The recurrent neural network 240 may generate suchadjusted signals y₁(n), y₂(n) 241, 243 according to the amplified datasignals x₁(n), x₂(n) 221, 223. The recurrent neural network 240 may bein communication with multiple (e.g. all) wireless transmitters paths ofthe electronic device 110 and all the respective compensation componentscoupled to respective wireless receivers, and may provide adjustedsignals based on transmitter output data signals and/or amplifier outputdata signals, such as amplified data signals x₁(n), x₂(n) 221, 223.

It may be desirable in some examples to compensate for self-interferencenoise to allow for and/or improve full duplex transmission. For example,it may be desirable for wireless transmitters 111,113 of the electronicdevice 110 to transmit wireless transmission signals at a certainfrequency band; and, at the same time or simultaneously, wirelessreceivers 105, 107 receive wireless transmission signals on a differentfrequency band. The recurrent neural network 240 may determine theself-interference contributed from each wireless transmission based onthe amplified signals to compensate for each received wirelesstransmission with an adjusted signal y₁(n) 241 and/or y₂(n) 243.

Particularly as wireless communications move toward or employ 5Gstandards, efficient use of wireless spectra may become increasinglyimportant. Accordingly, the adjusted signals y₁(n) 241 and y₂(n) 243 maycompensate for interference generated by one or more of the poweramplifiers 219, 229 at harmonic frequencies of certain frequenciesand/or at additional frequency components based on the frequency beingamplified at respective power amplifiers 219, 229. For example, with theamplified data signals x₁(n), x₂(n) 221, 223, the recurrent neuralnetwork may compensate for intermodulation components generated by thepower amplifiers 219, 229. Particularly as wireless communications movetoward 5G standards, efficient use of wireless spectra may becomeincreasingly important.

An intermodulation component may be generated internally in theelectronic device 110 by the difference of the two frequencies beingamplified or by the nonlinear characteristics of the power amplifiers219, 229. In an example, interference may be created in the 1.8 GHz bandfrom difference of two frequencies. The wireless transmitter 111modulates the data signal 211 to the 1.8 GHz band (e.g., the firstfrequency). The wireless transmitter 113 modulates the data signal 213to the 3.5 GHz band (e.g., the second frequency). Interference may becreated, by the power amplifiers 219, 229, at a frequency that is thedifference of these two frequencies (e.g., 1.7 GHz), which may causesome interference at the 1.8 GHZ band. Accordingly, if the first andsecond transmitter 111, 113 generate a difference frequency that isclose to the intended transmission frequency of the first or secondfrequency, the compensation components 245, 247 may be utilized tocompensate that interference utilizing the adjusted signals y₁(n) 241and y₂(n) 243.

Examples of recurrent neural networks described herein may provide theadjusted signals y₁(n), y₂(n) 241, 243 to receiver(s) and/ortransceiver(s). Compensation components 245, 247 may receive theadjusted signals y₁(n), y₂(n) 241, 243 and compensate for an incomingreceived wireless transmission from antennas 105, 107. For example, thecompensation components 245, 247 may combine the adjusted signals withthe incoming received wireless transmission in a manner whichcompensates for (e.g. reduces) self-interference. In some examples, thecompensation components 245, 247 may subtract the adjusted signalsy₁(n), y₂(n) 241, 243 from the received wireless transmission to producecompensated received signals for the respective wireless receivers 115,117. For example, the compensation components 245, 247 may beimplemented as adders and/or subtractors. The compensation components245, 247 may communicate the compensated received signals to thewireless receivers 115, 117.

The wireless receivers 115, 117 may process the compensated receivedsignal with the operations of a radio-frequency (RF) front-end. Thewireless receiver may process the compensated received signals as awireless receiver 400, described below with reference to FIG. 4, forexample. While the compensation components 245, 247 have been describedin terms of subtracting an adjusting signal from a received wirelesstransmission, it can be appreciated that various compensations may bepossible, such as adjusted signal that operates as a transfer functioncompensating the received wireless transmission or an adjusted signalthat operates as an optimization vector to multiply the receivedwireless transmission. Responsive to such compensation, electronicdevice 110 may transmit and receive wireless communications signals in afull duplex transmission mode.

Examples of recurrent neural networks described herein, including therecurrent neural network 240 of FIG. 2A may be implemented usinghardware, software, firmware, or combinations thereof. For example,recurrent neural network 240 may be implemented using circuitry and/orone or more processing unit(s) (e.g. processors) and memory encoded withexecutable instructions for causing the recurrent neural network toperform one or more functions described herein. FIG. 5A depicts anexemplary recurrent neural network.

FIG. 2B is a schematic illustration 250 of an electronic device 270arranged in accordance with examples described herein. Similarly,numbered elements of FIG. 2B include analogous functionality to thosenumbered elements of FIG. 2A. The electronic device 270 may also includerecurrent neural network 260, compensation component 265, andcompensation component 267. Each wireless transmitter 111, 113 may be incommunication with a respective antenna, such as antenna 101, antenna103 via respective power amplifiers, such as power amplifiers 219, 229.Each wireless transmitter 111, 113 receives a respective data signal,such as data signals 211, 213. The wireless transmitter 113 may processthe data signal 213 with the operations of a radio-frequency (RF)front-end to generate signal x₁(n) 220. The signal x₁(n) 220 may beamplified by the power amplifier 229 to generate amplified signal x₂(n)223.

The data signals x₁(n), x₂(n) 220, 223 are provided in the electronicdevice 110 to the recurrent neural network 260. For example, the datasignals x₁(n), x₂(n) 220, 223 may be provided to the recurrent neuralnetwork 260 via internal paths from an output of the wirelesstransmitter 113 and an output of the power amplifier 229. Accordingly,paths stemming from the wireless transmitter 113 and the recurrentneural network 260 may be in communication with one another. Therecurrent neural network 260, therefore, receives a first data signalx₁(n) 220 associated with the second frequency from the wirelesstransmitter 113 for the second frequency and a second amplified datasignal x₂(n) 223 associated with the second frequency the poweramplifier 229.

Recurrent neural network 260 and compensation components 265, 267 may bein communication with one another. Each wireless receiver may be incommunication with a respective antenna via a receiver path of therespective wireless receivers 115, 117, such as antenna 105, 107 via arespective compensation component, such as compensation component 265,267, and respective low-noise amplifiers (LNA) 249, 259. In someexamples, a wireless transmission received at antennas 105, 107 may becommunicated to wireless receiver 115, 117 after amplification of thereceived signal by LNAs 249, 259 and compensation of self-interferenceby the respective compensation component 265, 267. Each wirelessreceiver 115, 117 processes the received, amplified, and compensatedwireless transmission to produce a respective received data signal, suchas received data signals 255, 257. In other examples, fewer, additional,and/or different components may be provided.

Examples of recurrent neural networks described herein may generate andprovide adjusted signals to compensation components. So, for example,the recurrent neural network 260 may generate adjusted signals y₁(n),y₂(n) 261, 263 and provide such adjusted signals to the compensationcomponents 265, 267. The recurrent neural network 260 may generate suchadjusted signals y₁(n), y₂(n) 261, 263 based on the data signals x₁(n),x₂(n) 220, 223. The recurrent neural network 260 may be in communicationwith multiple (e.g. all) wireless transmitters paths of the electronicdevice 110 and all the respective compensation components coupled torespective wireless receivers, and may provide adjusted signals based ontransmitter output data signals and/or amplifier output data signals,such as data signals x₁(n), x₂(n) 220, 223.

It may be desirable in some examples to compensate for theself-interference noise to achieve full duplex transmission. Forexample, it may be desirable for wireless transmitters 111,113 of theelectronic device 110 to transmit wireless transmission signals at acertain frequency band; and, at the same time or simultaneously,wireless receivers 105, 107 receive wireless transmission signals on adifferent frequency band. The recurrent neural network 260 may determinethe self-interference contributed from each wireless transmission basedon the amplified signals to compensate for each received wirelesstransmission with an adjusted signal y₁(n), y₂(n) 261, 263. Accordingly,the adjusted signals y₁(n), y₂(n) 261, 263 may compensate forinterference generated by the power amplifier 229 at harmonicfrequencies of certain frequencies or additional frequency componentsderived from the frequency being amplified at the power amplifier 229.For example, with the data signals x₁(n), x₂(n) 220, 223, the recurrentneural network may compensate for harmonic frequencies generated by thepower amplifier 229.

A harmonic frequency may be generated internally in the electronicdevice 110 or by the nonlinear characteristics of the power amplifier229 based on the frequency being amplified. In an example, interferencemay be created in the 3.5 GHz band from a second-order harmonicfrequency of the frequency being amplified by the power amplifier 229.In an example, the first frequency of the wireless transmitter 111 maymodulate the data signal 211 to the 3.5 GHz band and the secondfrequency of the wireless transmitter 113 may modulate the data signal213 to the 1.8 GHz band. Accordingly, the power amplifier 229 mayamplify the modulated data signal x₁(n) 220 having a frequency in the1.8 GHz band, and may introduce harmonic components into the amplifieddata signal x₂(n) 223, such as a second-order harmonic component at 3.6GHz, which may interfere with the 3.5 GHz band. Accordingly, if thefirst and second transmitter 111, 113 generate a harmonic component thatis close to the intended transmission frequency of the first or secondfrequency, the compensation components 265, 267 may be utilized tocompensate that interference utilizing the adjusted signals y₁(n), y₂(n)261, 263.

While FIGS. 2A and 2B depict respective self-interference calculators240, 260 operating on data signals from the same or different paths ofwireless transmitters 111, 113 at varying frequencies, it can beappreciated that various paths with data signals, whether amplified,modulated, or initial, may be provided to a recurrent neural network,such as self-interference calculators 240, 260, to compensate for noisefrom interference generated by respective received transmission signals,such as transmission signals received at antennas 105, 107. For example,in an embodiment, a self-interference calculator may receive each of thedata signals being received in FIGS. 2A and 2B (e.g., data signals 221,223, and 220) and may provide adjusted signals to varying points in areceiver path, for example, as adjusted signals 241, 243, 261, and 263are provided to paths of wireless receiver 115 and/or wireless receiver117. Accordingly, the electronic devices 110, 270 of FIGS. 2A and 2B maybe utilized in system 100 as electronic devices 102 and/or 110 tocompensate for self-interference noise generated in transmitting datasignals from the electronic devices communicating in such a system.

FIG. 3 is a schematic illustration of a wireless transmitter 300. Thewireless transmitter 300 receives a data signal 311 and performsoperations to generate wireless communication signals for transmissionvia the antenna 303. The wireless transmitter 300 may be utilized toimplement the wireless transmitters 111, 113 in FIGS. 1, 2A, 2B, orwireless transmitters 131, 133 of FIG. 1, for example. The transmitteroutput data x_(N)(n) 310 is amplified by a power amplifier 332 beforethe output data are transmitted on an RF antenna 303. The operations tothe RF-front end may generally be performed with analog circuitry orprocessed as a digital baseband operation for implementation of adigital front-end. The operations of the RF-front end include ascrambler 304, a coder 308, an interleaver 312, a modulation mapping316, a frame adaptation 320, an IFFT 324, a guard interval 328, andfrequency up-conversion 330.

The scrambler 304 may convert the input data to a pseudo-random orrandom binary sequence. For example, the input data may be a transportlayer source (such as MPEG-2 Transport stream and other data) that isconverted to a Pseudo Random Binary Sequence (PRBS) with a generatorpolynomial. While described in the example of a generator polynomial,various scramblers 304 are possible.

The coder 308 may encode the data outputted from the scrambler to codethe data. For example, a Reed-Solomon (RS) encoder, turbo encoder may beused as a first coder to generate a parity block for each randomizedtransport packet fed by the scrambler 304. In some examples, the lengthof parity block and the transport packet can vary according to variouswireless protocols. The interleaver 312 may interleave the parity blocksoutput by the coder 308, for example, the interleaver 312 may utilizeconvolutional byte interleaving. In some examples, additional coding andinterleaving can be performed after the coder 308 and interleaver 312.For example, additional coding may include a second coder that mayfurther code data output from the interleaver, for example, with apunctured convolutional coding having a certain constraint length.Additional interleaving may include an inner interleaver that formsgroups of joined blocks. While described in the context of a RS coding,turbo coding, and punctured convolution coding, various coders 308 arepossible, such as a low-density parity-check (LDPC) coder or a polarcoder. While described in the context of convolutional byteinterleaving, various interleavers 312 are possible.

The modulation mapping 316 may modulate the data output from theinterleaver 312. For example, quadrature amplitude modulation (QAM) maybe used to map the data by changing (e.g., modulating) the amplitude ofthe related carriers. Various modulation mappings may be used,including, but not limited to: Quadrature Phase Shift Keying(QPSK), SCMANOMA, and MUSA (Multi-user Shared Access). Output from the modulationmapping 316 may be referred to as data symbols. While described in thecontext of QAM modulation, various modulation mappings 316 are possible.The frame adaptation 320 may arrange the output from the modulationmapping according to bit sequences that represent correspondingmodulation symbols, carriers, and frames.

The IFFT 324 may transform symbols that have been framed intosub-carriers (e.g., by frame adaptation 320) into time-domain symbols.Taking an example of a 5G wireless protocol scheme, the IFFT can beapplied as N-point IFFT:

$\begin{matrix}{x_{k} = {\sum\limits_{n = 1}^{N}{X_{n}e^{i\; 2\pi\;{{kn}/N}}}}} & (1)\end{matrix}$

where X_(n) is the modulated symbol sent in the nth 5G sub-carrier.Accordingly, the output of the IFFT 324 may form time-domain 5G symbols.In some examples, the IFFT 324 may be replaced by a pulse shaping filteror poly-phase filtering banks to output symbols for frequencyup-conversion 330.

In the example of FIG. 3, the guard interval 328 adds a guard intervalto the time-domain 5G symbols. For example, the guard interval may be afractional length of a symbol duration that is added, to reduceinter-symbol interference, by repeating a portion of the end of atime-domain 5G symbol at the beginning of the frame. For example, theguard interval can be a time period corresponding to the cyclic prefixportion of the 5G wireless protocol scheme.

The frequency up-conversion 330 may up-convert the time-domain 5Gsymbols to a specific radio frequency. For example, the time-domain 5Gsymbols can be viewed as a baseband frequency range and a localoscillator can mix the frequency at which it oscillates with the 5Gsymbols to generate 5G symbols at the oscillation frequency. A digitalup-converter (DUC) may also be utilized to convert the time-domain 5Gsymbols. Accordingly, the 5G symbols can be up-converted to a specificradio frequency for an RF transmission.

Before transmission, at the antenna 303, a power amplifier 332 mayamplify the transmitter output data x_(N)(n) 310 to output data for anRF transmission in an RF domain at the antenna 303. The antenna 303 maybe an antenna designed to radiate at a specific radio frequency. Forexample, the antenna 303 may radiate at the frequency at which the 5Gsymbols were up-converted. Accordingly, the wireless transmitter 300 maytransmit an RF transmission via the antenna 303 based on the data signal311 received at the scrambler 304. As described above with respect toFIG. 3, the operations of the wireless transmitter 300 can include avariety of processing operations. Such operations can be implemented ina conventional wireless transmitter, with each operation implemented byspecifically-designed hardware for that respective operation. Forexample, a DSP processing unit may be specifically-designed to implementthe IFFT 324. As can be appreciated, additional operations of wirelesstransmitter 300 may be included in a conventional wireless receiver.

FIG. 4 is a schematic illustration of wireless receiver 400. Thewireless receiver 400 receives input data X (i,j) 410 from an antenna405 and performs operations of a wireless receiver to generate receiveroutput data at the descrambler 444. The wireless receiver 400 may beutilized to implement the wireless receivers 115, 117 in FIGS. 1, 2A,2B, for example or wireless receivers 135, 137 of FIG. 1. The antenna405 may be an antenna designed to receive at a specific radio frequency.The operations of the wireless receiver may be performed with analogcircuitry or processed as a digital baseband operation forimplementation of a digital front-end. The operations of the wirelessreceiver include a frequency down-conversion 412, guard interval removal416, a fast Fourier transform (FFT) 420, synchronization 424, channelestimation 428, a demodulation mapping 432, a deinterleaver 436, adecoder 440, and a descrambler 444.

The frequency down-conversion 412 may down-convert the frequency domainsymbols to a baseband processing range. For example, continuing in theexample of a 5G implementation, the frequency-domain 5G symbols may bemixed with a local oscillator frequency to generate 5G symbols at abaseband frequency range. A digital down-converter (DDC) may also beutilized to convert the frequency domain symbols. Accordingly, the RFtransmission including time-domain 5G symbols may be down-converted tobaseband. The guard interval removal 416 may remove a guard intervalfrom the frequency-domain 5G symbols. The FFT 420 may transform thetime-domain 5G symbols into frequency-domain 5G symbols. Taking anexample of a 5G wireless protocol scheme, the FFT can be applied asN-point FFT:

$\begin{matrix}{X_{n} = {\sum\limits_{k = 1}^{N}{x_{k}e^{{- i}\; 2\pi\;{{kn}/N}}}}} & (2)\end{matrix}$

where X_(n) is the modulated symbol sent in the nth 5G sub-carrier.Accordingly, the output of the FFT 420 may form frequency-domain 5Gsymbols. In some examples, the FFT 420 may be replaced by poly-phasefiltering banks to output symbols for synchronization 424.

The synchronization 424 may detect pilot symbols in the 5G symbols tosynchronize the transmitted data. In some examples of a 5Gimplementation, pilot symbols may be detected at the beginning of aframe (e.g., in a header) in the time-domain. Such symbols can be usedby the wireless receiver 400 for frame synchronization. With the framessynchronized, the 5G symbols proceed to channel estimation 428. Thechannel estimation 428 may also use the time-domain pilot symbols andadditional frequency-domain pilot symbols to estimate the time orfrequency effects (e.g., path loss) to the received signal.

For example, a channel may be estimated according to N signals receivedthrough N antennas (in addition to the antenna 405) in a preamble periodof each signal. In some examples, the channel estimation 428 may alsouse the guard interval that was removed at the guard interval removal416. With the channel estimate processing, the channel estimation 428may compensate for the frequency-domain 5G symbols by some factor tominimize the effects of the estimated channel. While channel estimationhas been described in terms of time-domain pilot symbols andfrequency-domain pilot symbols, other channel estimation techniques orsystems are possible, such as a MIMO-based channel estimation system ora frequency-domain equalization system.

The demodulation mapping 432 may demodulate the data outputted from thechannel estimation 428. For example, a quadrature amplitude modulation(QAM) demodulator can map the data by changing (e.g., modulating) theamplitude of the related carriers. Any modulation mapping describedherein can have a corresponding demodulation mapping as performed bydemodulation mapping 432. In some examples, the demodulation mapping 432may detect the phase of the carrier signal to facilitate thedemodulation of the 5G symbols. The demodulation mapping 432 maygenerate bit data from the 5G symbols to be further processed by thedeinterleaver 436.

The deinterleaver 436 may deinterleave the data bits, arranged as parityblock from demodulation mapping into a bit stream for the decoder 440,for example, the deinterleaver 436 may perform an inverse operation toconvolutional byte interleaving. The deinterleaver 436 may also use thechannel estimation to compensate for channel effects to the parityblocks.

The decoder 440 may decode the data outputted from the scrambler to codethe data. For example, a Reed-Solomon (RS) decoder or turbo decoder maybe used as a decoder to generate a decoded bit stream for thedescrambler 444. For example, a turbo decoder may implement a parallelconcatenated decoding scheme. In some examples, additional decodingand/or deinterleaving may be performed after the decoder 440 anddeinterleaver 436. For example, additional decoding may include anotherdecoder that may further decode data output from the decoder 440. Whiledescribed in the context of a RS decoding and turbo decoding, variousdecoders 440 are possible, such as low-density parity-check (LDPC)decoder or a polar decoder.

The descrambler 444 may convert the output data from decoder 440 from apseudo-random or random binary sequence to original source data. Forexample, the descrambler 44 may convert decoded data to a transportlayer destination (e.g., MPEG-2 transport stream) that is descrambledwith an inverse to the generator polynomial of the scrambler 304. Thedescrambler thus outputs receiver output data. Accordingly, the wirelessreceiver 400 receives an RF transmission including input data X (i,j)410 via to generate the receiver output data.

As described herein, for example with respect to FIG. 4, the operationsof the wireless receiver 400 can include a variety of processingoperations. Such operations can be implemented in a conventionalwireless receiver, with each operation implemented byspecifically-designed hardware for that respective operation. Forexample, a DSP processing unit may be specifically-designed to implementthe FFT 420. As can be appreciated, additional operations of wirelessreceiver 400 may be included in a conventional wireless receiver.

FIG. 5A is a schematic illustration of an example neural network 500arranged in accordance with examples described herein. The neuralnetwork 500 includes a network of processing elements 504, 506, 509 thatoutput adjusted signals y₁(n), y₂(n), y₃(n), y_(L)(n) 508 based onamplified signals x₁(n), x₂(n), x₃(n), x_(N)(n) 502. For example, theamplified signals x₁(n), x₂(n), x₃(n), x_(N)(n) 502 may correspond toinputs for respective antennas of each transmitter generating therespective x₁(n), x₂(n), x₃(n), x_(N)(n) 502. The processing elements504 receive the amplified signals x₁(n), x₂(n), x₃(n), x_(N)(n) 502 asinputs.

The processing elements 504 may be implemented, for example, using bitmanipulation units that may forward the amplified signals x₁(n), x₂(n),x₃(n), x_(N)(n) 502 to processing elements 506. In some implementations,a bit manipulation unit may perform a digital logic operation on abitwise basis. For example, a bit manipulation unit may be a NOT logicunit, an AND logic unit, an OR logic unit, a NOR logic unit, a NANDlogic unit, or an XOR logic unit. Processing elements 506 may beimplemented, for example, using multiplication units that include anon-linear vector set (e.g., center vectors) based on a non-linearfunction, such as a Gaussian function

$( {{e.g.},\text{:}^{{f{(r)}} = {\exp({- \frac{r^{2}}{\sigma^{2}}})}},} $

a multi-quadratic function (e.g., f(r)=(r²+σ¹)), an inversemulti-quadratic function (e.g., f(r)=(r²+σ²)), a thin-plate spinefunction (e.g., f(r)=r² log(r)), a piece-wise linear function (e.g.,f(r)=½(|r+1|−|r−1|), or a cubic approximation function (e.g.,f(r)=½(|r³+1|−|r³−1|)). In some examples, the parameter σ is a realparameter (e.g., a scaling parameter) and r is the distance between theinput signal (e.g., x₁(n), x₂(n), x₃(n), x_(N)(n) 502) and a vector ofthe non-linear vector set. Processing elements 509 may be implemented,for example, using accumulation units that sum the intermediateprocessing results received from each of the processing elements 506. Incommunicating the intermediate processing results, each intermediateprocessing result may be weighted with a weight ‘W’. For example, themultiplication processing units may weight the intermediate processingresults based on a minimized error for the all or some of the adjustmentsignals that may generated by a neural network.

The processing elements 506 include a non-linear vector set that may bedenoted as C_(i) (for i=1, 2, . . . H). H may represent the number ofprocessing elements 506. With the amplified signals x₁(n), x₂(n), x₃(n),x_(N)(n) 502 received as inputs to processing elements 506, afterforwarding by processing elements 504, the output of the processingelements 506, operating as multiplication processing units, may beexpressed as h_(i)(n), such that:

h _(i)(n)=f ₁(∥X(n)−C _(i)∥) (i=1,2, . . . ,H)  (3)

f_(i) may represent a non-linear function that is applied to themagnitude of the difference between x₁(n), x₂(n), x₃(n), x_(N)(n) 502and the center vectors C_(i). The output h_(i)(n) may represent anon-linear function such as a Gaussian function, multi-quadraticfunction, an inverse multi-quadratic function, a thin-plate spinefunction, or a cubic approximation function.

The output h_(i)(n) of the processing elements 506 may be weighted witha weight matrix ‘W’. The output h_(i)(n) of the processing elements 506can be referred to as intermediate processing results of the neuralnetwork 500. For example, the connection between the processing elements506 and processing elements 509 may be a linear function such that thesummation of a weighted output h_(i)(n) such that the adjusted signalsy₁(n), y₂(n), y₃(n), y_(L)(n) 508 may be expressed, in Equation 4 as:

y _(i)(n)=Σ_(j=1) ^(H) W _(ij) h _(j)(n)=Σ_(j=1) ^(H) W _(ij) f_(j)(∥X(n)−C _(j)∥) (i=1,2, . . . ,L)  (4)

Accordingly, the adjusted signals y₁(n), y₂(n), y₃(n), y_(L)(n) 508 maybe the output y_(i)(n) of the i'th processing element 509 at time n,where L is the number of processing elements 509. W_(ij) is theconnection weight between j'th processing element 506 and i'thprocessing element 509 in the output layer. For example, as describedwith respect to the example of FIG. 6A, the center vectors C and theconnection weights W_(ij) of each layer of processing elements may bedetermined by a training unit 645 that utilizes sample vectors 660 totrain a recurrent neural network 640. Advantageously, the adjustedsignals y₁(n), y₂(n), y₃(n), y_(L)(n) 508 generated from the amplifiedsignals x₁(n), x₂(n), x₃(n), x_(N)(n) 502 may be computed with near-zerolatency such that self-interference compensation may be achieved in anyelectronic device including a neural network, such as the neural network500. A wireless device or system that implements a neural network 500may achieve full duplex transmission. For example, the adjusted signalsgenerated by the neural network 500 may compensate self-interferencethat an antenna of the wireless device or system will experience due totransmission signals (e.g., amplified signals) by a power amplifier ofthe wireless device or system.

While the neural network 500 has been described with respect to a singlelayer of processing elements 506 that include multiplication units, itcan be appreciated that additional layers of processing elements withmultiplication units may be added between the processing elements 504and the processing elements 509. The neural network is scalable inhardware form, with additional multiplication units being added toaccommodate additional layers. Using the methods and systems describedherein, additional layer(s) of processing elements includingmultiplication processing units and the processing elements 506 may beoptimized to determine the center vectors C and the connection weightsWO of each layer of processing elements including multiplication units.In some implementations, for example as described with reference toFIGS. 5C-5E, layers of processing elements 506 may includemultiplication/accumulation (MAC) units, with each layer havingadditional MAC units. Such implementations, having accumulated theintermediate processing results in a respective processing elements(e.g., the respective MAC unit), may also include memory look-up (MLU)units that are configured to retrieve a plurality of coefficients andprovide the plurality of coefficients as the connection weights for therespective layer of processing elements 506 to be mixed with the inputdata.

The neural network 500 can be implemented using one or more processors,for example, having any number of cores. An example processor core caninclude an arithmetic logic unit (ALU), a bit manipulation unit, amultiplication unit, an accumulation unit, a multiplication/accumulation(MAC) unit, an adder unit, a look-up table unit, a memory look-up unit,or any combination thereof. In some examples, the neural network 240 mayinclude circuitry, including custom circuitry, and/or firmware forperforming functions described herein. For example, circuitry caninclude multiplication unit, accumulation units, MAC units, and/or bitmanipulation units for performing the described functions, as describedherein. The neural network 240 may be implemented in any type ofprocessor architecture including but not limited to a microprocessor ora digital signal processor (DSP), or any combination thereof.

FIG. 5B is a schematic illustration of a recurrent neural networkarranged in accordance with examples described herein. The recurrentneural network 170 include three stages (e.g., layers): an inputs stage171; a combiner stage 173 and 175, and an outputs stage 177. While threestages are shown in FIG. 5B, any number of stages may be used in otherexamples, e.g., as described with reference to FIGS. 5C-5E. In someimplementations, the recurrent neural network 170 may have multiplecombiner stages such that outputs from one combiner stage is provided toanother combiners stage, until being providing to an outputs stage 177.As described with reference to FIG. 5C, for example, there may bemultiple combiner stages in a neural network 170. As depicted in FIG.5B, the delay units 175 a, 175 b, and 175 c may be optional componentsof the neural network 170. When such delay units 175 a, 175 b, and 175 care utilized as described herein, the neural network 170 may be referredto as a recurrent neural network. Accordingly, the recurrent neuralnetwork 170 can be used to implement any of the recurrent neuralnetworks described herein; for example, the recurrent neural network 240of FIG. 2A or recurrent neural network 260 of FIG. 2B.

The first stage of the neural network 170 includes inputs node 171. Theinputs node 171 may receive input data at various inputs of therecurrent neural network. The second stage of the neural network 170 isa combiner stage including combiner units 173 a, 173 b, 173 c; and delayunits 175 a, 175 b, 175 c. Accordingly, the combiner units 173 and delayunits 175 may be collectively referred to as a stage of combiners.Accordingly, in the example of FIG. 5C with recurrent neural network 512implementing such combiners, such a recurrent neural network 512 canimplements the combiner units 173 a-c and delay units 175 a-c in thesecond stage. In such an implementation, the recurrent neural network512 may perform a nonlinear activation function using the input datafrom the inputs node 171 (e.g., input signals X1(n), X2(n), and X3(n)).The third stage of neural network 170 includes the outputs node 177.Additional, fewer, and/or different components may be used in otherexamples. Accordingly, the recurrent neural network 170 can be used toimplement any of the recurrent neural networks described herein; forexample, any of the recurrent neural networks 512 of FIGS. 5C-5E.

The recurrent neural network 170 includes delay units 175 a, 175 b, and175 c, which generate delayed versions of the output from the respectivecombiner units 173 a-c based on receiving such output data from therespective combiner units 173 a-c. In the example, the output data ofcombiner units 173 a-c may be represented as h(n); and, accordingly,each of the delay units 175 a-c delay the output data of the combinerunits 173 a-c to generate delayed versions of the output data from thecombiner units 173 a-c, which may be represented as h(n−t). In variousimplementations, the amount of the delay, t, may also vary, e.g., oneclock cycle, two clock cycles, or one hundred clock cycles. That is, thedelay unit 175 may receive a clock signal and utilize the clock signalto identify the amount of the delay. In the example of FIG. 5B, thedelayed versions are delayed by one time period, where ‘1’ represents atime period. A time period may corresponds to any number of units oftime, such as a time period defined by a clock signal or a time perioddefined by another element of the neural network 170. In variousimplementations, the delayed versions of the output from the respectivecombiner units 173 a-c may be referred to as signaling that is based onthe output data of combiner units 173 a-c.

Continuing in the example of FIG. 5B, each delay unit 175 a-c providesthe delayed versions of the output data from the combiner units 173 a-cas input to the combiner units 173 a-c, to operate, optionally, as arecurrent neural network. Such delay units 175 a-c may providerespective delayed versions of the output data from nodes of thecombiner units 173 a-c to respective input units/nodes of the combinerunits 173 a-c. In utilizing delayed versions of output data fromcombiner units 173 a-c, the recurrent neural network 170 may trainweights at the combiner units 173 a-c that incorporate time-varyingaspects of input data to be processed by such a recurrent neural network170. Once trained, in some examples, the inputs node 171 receiveswireless data to be transmitted from nonlinear power amplifiers, whichcreate transmissions at frequencies that interfere with otherfrequencies of interest (e.g., a 5G protocol frequency). Accordingly,the input nodes 171 receive such amplified signals (e.g., output datax₁(n), x₂(n) 221, 223) and process that input data in the recurrentneural network 170 a. Each stream of input data may correspond to asignal to be transmitted (e.g., the amplified signals) at correspondingantennas (e.g., antennas 101 and 103 FIG. 1). Accordingly, because anRNN 170 incorporates the delayed versions of output data from combinerunits 173 a-c, the time-varying nature of the input data may providefaster and more efficient processing of the input data.

Examples of recurrent neural network training and inference can bedescribed mathematically. Again, as an example, consider input data at atime instant (n), given as: X(n)=[x₁(n), x₂(n), . . . x_(m)(n)]^(T). Thecenter vector for each element in hidden layer(s) of the recurrentneural network 170 (e.g., combiner units 173 a-c) may be denoted asC_(i) (for i=12, . . . , H, where H is the element number in the hiddenlayer).

The output of each element in a hidden layer may then be given as:

h _(i)(n)=f _(i)(∥X(n)+h _(i)(n−t)−C _(i)∥) for (i=1,2, . . . ,H)  (5)

t may be the delay at the delay unit 175 such that the output of thecombiner units 173 includes a delayed version of the output of thecombiner units 173. In some examples, this may be referred to asfeedback of the combiner units 173. Accordingly, each of the connectionsbetween a last hidden layer and the output layer may be weighted. Eachelement in the output layer may have a linear input-output relationshipsuch that it may perform a summation (e.g., a weighted summation).Accordingly, an output of the i'th element in the output layer at time nmay be written as:

$\begin{matrix}{{y_{i}(n)} = {{{\sum\limits_{j = 1}^{H}{W_{ij}{h_{j}(n)}}} + {W_{ij}{h_{j}( {n - t} )}}} = {\sum\limits_{j = 1}^{H}{W_{ij}{f_{j}( {{{X(n)} + {h_{i}( {n - t} )} - C_{j}}} )}}}}} & (6)\end{matrix}$

for (=1, 2, . . . , L) and where L is the element number of the outputof the output layer and W_(ij) is the connection weight between the j'thelement in the hidden layer and the i'th element in the output layer.

Additionally or alternatively, while FIG. 5B has been described withrespect to a single stage of combiners (e.g., second stage) includingthe combiner units 173 a-c and delay units 175 a-c, it can beappreciated that multiple stages of similar combiner stages may beincluded in the neural network 170 with varying types of combiner unitsand varying types of delay units with varying delays, for example, aswill now be described with reference to FIGS. 5C-5E.

FIG. 5C is a schematic illustration of a recurrent neural network 512arranged in a system 501 in accordance with examples described herein.Such a hardware implementation (e.g., system 501) may be used, forexample, to implement one or more neural networks, such as the recurrentneural network 240 of FIG. 2, the neural network 500 of FIG. 5A orrecurrent neural network 170 of FIG. 5B. Additionally or alternatively,in some implementations, the recurrent neural network 512 may receiveinput data 510 a, 510 b, and 510 c from such a computing system. Theinput data 510 a, 510 b, and 510 c may be data to be transmitted, whichmay be stored in a memory 545. In some examples, data stored in thememory 545 may be input data to be transmitted from a plurality ofantennas coupled to an electronic device 110 in which the recurrentneural network 512 is implemented. In an example in which the electronicdevice 110 is coupled to the plurality of antennas 101 and 103, theinput data 510 a X₁(i, i−1) may correspond to a first RF transmission tobe transmitted at the antenna 101 at a first frequency; the input data510 b X₂(i, i−1) may correspond to a second RF transmission to betransmitted at the antenna 103 at a second frequency; and the input data510 c X_(m)(i, i−1) may correspond to a m'th RF transmission to betransmitted at an m'th antenna at a m'th frequency. m may represent thenumber of antennas, with each antenna transmitting a portion of inputdata.

In some examples, m may also correspond to a number of wireless channelsover which the input data is to be transmitted; for example, in a MIMOtransmission, an RF transmission may be sent over multiple wirelesschannels at the plurality of antennas 101 and 103. In an example of theinput data being received (in contrast to being transmitted), the inputdata 510 a, 510 b, 510 c may correspond to portions of input data to betransmitted at multiple antennas after having been processed bynonlinear power amplifiers 219, 229, for example. Accordingly, theoutput data 530 B(1) may be a MIMO output signal to be transmitted atthe antennas 101 and 103 at an electronic device that is implementingthe recurrent neural network 512 of the computing system 501.

As denoted in the representation of the input data signals, the inputdata 510 a X₁(i, i−1) includes a current portion of the input data, attime i, and a previous portion of the input data, at time i−1. Forexample, a current portion of the input data may be a sample obtained atthe antenna 101 at a certain time period (e.g., at time i), while aprevious portion of the input data may be a sample obtained at theantenna 101 at a time period previous to the certain time period (e.g.,at time i−1). Accordingly, the previous portion of the input data may bereferred to as a time-delayed version of the current portion of theinput data. The portions of the input data at each time period may beobtained in a vector or matrix format, for example. In an example, acurrent portion of the input data, at time i, may be a single value; anda previous portion of the input data, at time i−1, may be a singlevalue. Thus, the input data 510 a X₁(i, i−1) may be a vector. In someexamples, the current portion of the input data, at time i, may be avector value; and a previous portion of the input data, at time i−1, maybe a vector value. Thus, the input data 510 a X₁(i, i−1) may be amatrix.

Such input data, which is obtained with a current and previous portionof input data, may be representative of a Markov process, such that acausal relationship between at least the current sample and the previoussample may improve the accuracy of weight estimation for training ofcoefficient data to be utilized by the MAC units and MLUs of therecurrent neural network 512. As noted previously, the input data 510may represent data to be transmitted (e.g., amplified signals) at afirst frequency and/or data to be transmitted at a first wirelesschannel. Accordingly, the input data 510 b X₂(i, i−1) may represent datato be transmitted at a second frequency or at a second wireless channel,including a current portion of the input data, at time i, and a previousportion of the input data, at time i−1. And, the number of input signalsto be transmitted by the recurrent neural network 512 may equal in someexamples to a number of antennas coupled to an electronic device 110implementing the recurrent neural network 512. Accordingly, the inputdata 510 c X_(m)(i, i−1) may represent data to be transmitted at a m'thfrequency or at a m'th wireless channel, including a current portion ofthe input data, at time i, and a previous portion of the input data, attime i−1.

The recurrent neural network 512 may include multiplicationunit/accumulation (MAC) units 511 a-c, 516 a-b, and 520; delay units 513a-c, 517 a-b, and 521; and memory lookup units (MLUs) 514 a-c, 518 a-b,and 522 that, when mixed with input data to be transmitted from thememory 545, may generate output data (e.g. B (1)) 530. Each set of MACunits and MLU units having different element numbers may be referred toas a respective stage of combiners for the recurrent neural network 512.For example, a first stage of combiners includes MAC units 511 a-c andMLUs 514 a-c, operating in conjunction with delay units 513 a-c, to forma first stage or “layer,” as referenced with respect to FIG. 5A having“hidden” layers as various combiner stages. Continuing in the example,the second stage of combiners includes MAC units 516 a-b and MLUs 518a-b, operating in conjunction with delay units 517 a-b, to form a secondstage or second layer of hidden layers. And the third stage of combinersmay be a single combiner including the MAC unit 520 and MLU 522,operating in conjunction with delay unit 521, to form a third stage orthird layer of hidden layers.

The recurrent neural network 512, may be provided instructions 515,stored at the mode configurable control 505, to cause the recurrentneural network 512 to configure the multiplication units 511 a-c, 516a-c, and 520 to multiply and/or accumulate input data 510 a, 510 b, and510 c and delayed versions of processing results from the delay units513 a-c, 517 a-b, and 521 (e.g., respective outputs of the respectivelayers of MAC units) with coefficient data to generate the output data530 B(1). For example, the mode configurable control 505 may executeinstructions that cause the memory 545 to provide coefficient data(e.g., weights and/or other parameters) stored in the memory 545 to theMLUs 514 a-c, 518 a-b, and 522 as weights for the MAC units 511 a-c, 516a-b, and 520 and delay units 513 a-c, 517 a-b, and 521. Duringoperation, the mode configuration control 505 may be used to selectweights and/or other parameters in memory 545 based on an indicatedself-interference noise to calculate, e.g., the self-interference noisefrom a certain transmitting antenna to another transmitting antenna.

As denoted in the representation of the respective outputs of therespective layers of MAC units (e.g., the outputs of the MLUs 514 a-c,518 a-b, and 522), the input data to each MAC unit 511 a-c, 516 a-b, and520 includes a current portion of input data, at time i, and a delayedversion of a processing result, at time i−1. For example, a currentportion of the input data may be a sample obtained at the antenna 101 ata certain time period (e.g., at time i), while a delayed version of aprocessing result may be obtained from the output of the delay units 513a-c, 517 a-b, and 521, which is representative of a time period previousto the certain time period (e.g., as a result of the introduced delay).Accordingly, in using such input data, obtained from both a currentperiod and at least one previous period, output data B(1) 530 may berepresentative of a Markov process, such that a causal relationshipbetween at least data from a current time period and a previous timeperiod may improve the accuracy of weight estimation for training ofcoefficient data to be utilized by the MAC units and MLUs of therecurrent neural network 512 or inference of signals to be transmittedin utilizing the recurrent neural network 512. As noted previously, theinput data 510 may represent amplified signals x₁(n), x₂(n) 221, 223.Accordingly, the input data 510 b X₂(i, i−1) may represent amplifiedsignal x₂(n) 223. And, the number of input signals obtained by therecurrent neural network 512 may equal in some examples to a number ofantennas coupled to an electronic device 110 implementing the recurrentneural network 512. Accordingly, the input data 510 c X_(m)(i, i−1) mayrepresent data obtained at a m'th frequency or at a m'th wirelesschannel, including a current portion of the input data, at time i.Accordingly, in utilizing delayed versions of output data from 513 a-c,517 a-b, and 521 the recurrent neural network 170 providesindividualized frequency-band, time-correlation data for processing ofsignals to be transmitted.

In an example of executing such instructions 515 for mixing input datawith coefficients, at a first layer of the MAC units 511 a-c and MLUs514 a-c, the multiplication unit/accumulation units 511 a-c areconfigured to multiply and accumulate at least two operands fromcorresponding input data 510 a, 510 b, or 510 c and an operand from arespective delay unit 513 a-c to generate a multiplication processingresult that is provided to the MLUs 514 a-c. For example, themultiplication unit/accumulation units 511 a-c may perform amultiply-accumulate operation such that three operands, M N, and T aremultiplied with respective coefficient data, and then added with P togenerate a new version of P that is stored in its respective MLU 514a-c. Accordingly, the MLU 514 a latches the multiplication processingresult, until such time that the stored multiplication processing resultis be provided to a next layer of MAC units. The MLUs 514 a-c, 518 a-b,and 522 may be implemented by any number of processing elements thatoperate as a memory look-up unit such as a D, T, SR, and/or JK latches.

Additionally in the example, the MLU 514 a provides the processingresult to the delay unit 513 a. The delay unit 513 a delays theprocessing result (e.g., h1(i)) to generate a delayed version of theprocessing result (e.g, h_(i)(i−1)) to output to the MAC unit 511 a asoperand T. While the delay units 513 a-c, 517 a-b, and 521 of FIG. 5Care depicted introducing a delay of ‘1’, it can be appreciated thatvarying amounts of delay may be introduced to the outputs of first layerof MAC units. For example, a clock signal that introduced a sample delayof ‘l’ (e.g., h1(i−1)) may instead introduce a sample delay of ‘2’, ‘4’,or ‘100’. In various implementations, the delay units 513 a-c, 517 a-b,and 521 may correspond to any number of processing units that canintroduce a delay into processing circuitry using a clock signal orother time-oriented signal, such as flops (e.g., D-flops) and/or one ormore various logic gates (e.g., AND, OR, NOR, etc. . . . ) that mayoperate as a delay unit.

In the example of a first hidden layer of a recurrent neural network,the MLUs 514 a-c may retrieve coefficient data stored in the memory 545,which may be weights associated with weights to be applied to the firstlayer of MAC units to both the data from the current period and datafrom a previous period (e.g., the delayed versions of first layerprocessing results). For example, the MLU 514 a can be a table look-upthat retrieves one or more coefficients (e.g., specific coefficientsassociated with a first frequency) to be applied to both operands M andN, as well as an additional coefficient to be applied to operand T. TheMLUs 514 a-c also provide the generated multiplication processingresults to the next layer of the MAC units 516 a-b and MLUs 518 a-b. Theadditional layers of the MAC units 516 a, 516 b and MAC unit 520 workingin conjunction with the MLUs 518 a, 518 b and MLU 522, respectively, maycontinue to process the multiplication results to generate the outputdata 530 B(n). Using such a circuitry arrangement, the output data 530B(1) may be generated from the input data 510 a, 510 b, and 510 c.

Advantageously, the recurrent neural network 512 of system 501 mayutilize a reduced number of MAC units and/or MLUs, e.g., as compared tothe recurrent neural network 512 of FIG. 5D. The number of MAC units andMLUs in each layer of the recurrent neural network 512 is associatedwith a number of channels and/or a number of antennas coupled to adevice in which the recurrent neural network 512 is being implemented.For example, the first layer of the MAC units and MLUs may include mnumber of those units, where m represents the number of antennas, eachantenna receiving a portion of input data. Each subsequent layer mayhave a reduced portion of MAC units, delay units, and MLUs. As depicted,in FIG. 5C for example, a second layer of MAC units 516 a-b, delay unit517 a-b, and MLUs 518 a-b may include m−1 MAC units and MLUs, when m=3.Accordingly, the last layer in the recurrent neural network 512,including the MAC unit 520, delay unit 521, and MLU 522, includes onlyone MAC, one delay unit, and one MLU. Because the recurrent neuralnetwork 512 utilizes input data 510 a, 510 b, and 510 c that mayrepresent a Markov process, the number of MAC units and MLUs in eachsubsequent layer of the processing unit may be reduced, without asubstantial loss in precision as to the output data 530 B(1); forexample, when compared to a recurrent neural network 512 that includesthe same number of MAC units and MLUs in each layer, like that ofrecurrent neural network 512 of system 550.

The coefficient data, for example from memory 545, can be mixed with theinput data 510 a-510 c and delayed version of processing results togenerate the output data 530 B(1). For example, the relationship of thecoefficient data to the output data 530 B(1) based on the input data 510a-c and the delayed versions of processing results may be expressed as:

B(1)=a ¹ *f(Σ_(j=1) ^(m-1) a ^((m-1)) f _(j)(Σ_(k=1) ^(m) a ^((m)) X_(k)(i)))  (7)

where a(m), a(m−1), a1 are coefficients for the first layer ofmultiplication/accumulation units 511 a-c and outputs of delay units 513a-c; the second layer of multiplication/accumulation units 516 a-b andoutputs of delay units 517 a-b; and last layer with themultiplication/accumulation unit 520 and output of delay unit 521,respectively; and where f(●) is the mapping relationship which may beperformed by the memory look-up units 514 a-c and 518 a-b. As describedabove, the memory look-up units 514 a-c and 518 a-b retrievecoefficients to mix with the input data and respective delayed versionsof each layer of MAC units. Accordingly, the output data may be providedby manipulating the input data and delayed versions of the MAC unitswith the respective multiplication/accumulation units using a set ofcoefficients stored in the memory. The set of coefficients may beassociated with vectors representative of self-interference noise. Forexample, in the case of signals to be transmitted, each coefficient of aset of coefficients may be an individual vector of self-interference ofa respective wireless path to a first transmitting antenna of theplurality of antennas from at least one other transmitting antenna ofthe plurality of transmitting antennas. The set of coefficients may bebased on connection weights obtained from the training of a recurrentneural network (e.g., recurrent neural network 170). The resultingmapped data may be manipulated by additional multiplication/accumulationunits and additional delay units using additional sets of coefficientsstored in the memory associated with the desired wireless protocol. Thesets of coefficients multiplied at each stage of the recurrent neuralnetwork 512 may represent or provide an estimation of the processing ofthe input data in specifically-designed hardware (e.g., an FPGA).

Further, it can be shown that the system 501, as represented by Equation(7), may approximate any nonlinear mapping with arbitrarily small errorin some examples and the mapping of system 501 may be determined by thecoefficients a(m), a(m−1), a1. For example, if such coefficient data isspecified, any mapping and processing between the input data 510 a-510 cand the output data 530 may be accomplished by the system 501. Forexample, the coefficient data may represent non-linear mappings of theinput data 510 a-c to the output data B(1) 530. In some examples, thenon-linear mappings of the coefficient data may represent a Gaussianfunction, a piece-wise linear function, a sigmoid function, athin-plate-spline function, a multi-quadratic function, a cubicapproximation, an inverse multi-quadratic function, or combinationsthereof. In some examples, some or all of the memory look-up units 514a-c, 518 a-b may be deactivated. For example, one or more of the memorylook-up units 514 a-c, 518 a-b may operate as a gain unit with the unitygain. Such a relationship, as derived from the circuitry arrangementdepicted in system 501, may be used to train an entity of the computingsystem 501 to generate coefficient data. For example, using Equation(7), an entity of the computing system 501 may compare input data to theoutput data to generate the coefficient data.

Each of the multiplication unit/accumulation units 511 a-c, 516 a-b, and520 may include multiple multipliers, multiple accumulation unit, orand/or multiple adders. Any one of the multiplication unit/accumulationunits 511 a-c, 516 a-b, and 520 may be implemented using an ALU. In someexamples, any one of the multiplication unit/accumulation units 511 a-c,516 a-b, and 520 can include one multiplier and one adder that eachperform, respectively, multiple multiplications and multiple additions.The input-output relationship of a multiplication/accumulation unit 511a-c, 516 a-b, and 520 may be represented as:

$\begin{matrix}{B_{out} = {\sum\limits_{i = 1}^{I}{C_{i}*{B_{in}(1)}}}} & (8)\end{matrix}$

where “I” represents a number to perform the multiplications in thatunit, C the coefficients which may be accessed from a memory, such asmemory 545, and B_(in)(i) represents a factor from either the input data510 a-c or an output from multiplication unit/accumulation units 511a-c, 516 a-b, and 520. In an example, the output of a set ofmultiplication unit/accumulation units, B_(out), equals the sum ofcoefficient data, C_(i) multiplied by the output of another set ofmultiplication unit/accumulation units, B_(in)(i). B_(in)(i) may also bethe input data such that the output of a set of multiplicationunit/accumulation units, B_(out), equals the sum of coefficient data,C_(i) multiplied by input data.

While described in FIG. 5C as a recurrent neural network 512, it can beappreciated that the recurrent neural network 512 may be implemented inor as any of the recurrent neural networks described herein, inoperation to cancel and/or compensate self-interference noise via thecalculation of such noise as implemented in a recurrent neural network.For example, the recurrent neural network 512 can be used to implementany of the recurrent neural networks described herein; for example, therecurrent neural network 240 of FIG. 2A or recurrent neural network 260of FIG. 2B. In such implementations, recurrent neural networks may beused to reduce and/or improve errors which may be introduced byself-interference noise. Advantageously, with such an implementation,wireless systems and devices implementing such RNNs increase capacity oftheir respective wireless networks because additional data may betransmitted in such networks, which would not otherwise be transmitteddue to the effects of self-interference noise.

FIG. 5D is a schematic illustration of a recurrent neural network 512arranged in a system 550 in accordance with examples described herein.Such a hardware implementation (e.g., system 550) may be used, forexample, to implement one or more neural networks, such as the recurrentneural network 240 of FIG. 2, the neural network 500 of FIG. 5A, orrecurrent neural network 170 of FIG. 5B. Similarly described elements ofFIG. 5D may operate as described with respect to FIG. 5C, but may alsoinclude additional features as described with respect to FIG. 5D. Forexample, FIG. 5D depicts MAC units 562 a-c and delay units 563 a-c thatmay operate as described with respect MAC units 511 a-c and delay units513 a-c of FIG. 5C. Accordingly, elements of FIG. 5D, whose numericalindicator is offset by 50 with respect to FIG. 5C, include similarlyelements of the recurrent neural network 512; e.g., MAC unit 566 aoperates similarly with respect to MAC unit 516 a. The system 550,including recurrent neural network 512, also includes additionalfeatures not highlighted in the recurrent neural network 512 of FIG. 5C.For example, the recurrent neural network 512 of FIG. 5D additionallyincludes MAC units 566 c and 570 b-c; delay units 567 c and 571 b-c; andMLUs 568 c and 572 b-c, such that the output data is provided as 575a-c, rather than as singularly in FIG. 5C as B(1) 530. Advantageously,the system 550 including a recurrent neural network 512 may process theinput data 560 a-c to generate the output data 575 a-c with greaterprecision. For example, the recurrent neural network 512 may process theinput data 560 a-560 c with additional coefficient retrieved at MLU 568c and multiplied and/or accumulated by additional MAC units 566 c and570 b-c and additional delay units 567 c and 571 b-c, to generate outputdata 575 a-c with greater precision. For example, such additionalprocessing may result in output data that is more precise with respectproviding output data that estimates a vector representative ofself-interference noise between two different antennas. Inimplementations where board space (e.g., a printed circuit board) is nota primary factor in design, implementations of the recurrent neuralnetwork 512 of FIG. 5D may be desirable as compared to that of recurrentneural network 512 of FIG. 5C; which, in some implementations may occupyless board space as a result of having fewer elements than the recurrentneural network 512 of FIG. 5D.

FIG. 5E is a schematic illustration of a recurrent neural network 512arranged in a system 580 in accordance with examples described herein.Such a hardware implementation (e.g., system 580) may be used, forexample, to implement one or more neural networks, such as the recurrentneural network 240 of FIG. 2, neural network 500 of FIG. 5A, orrecurrent neural network 170 of FIG. 5B. Similarly described elements ofFIG. 5E may operate as described with respect to FIG. 5D, except for thedelay units 563 a-c, 567 a-c, and 571 a-c of FIG. 5D. For example, FIG.5E depicts MAC units 582 a-c and delay units 583 a-c that may operate asdescribed with respect to MAC units 562 a-c and delay units 563 a-c ofFIG. 5D. Accordingly, elements of FIG. 5E, whose numerical indicator isoffset by 20 with respect to FIG. 5D, include similarly elements of therecurrent neural network 512; e.g., MAC unit 586 a operates similarlywith respect to MAC unit 566 a.

The system 580, including recurrent neural network 512, also includesadditional features not highlighted in the recurrent neural network 512of FIG. 5D. Different than FIG. 5D, FIG. 5E depicts delay units 583 a,583 b, and 583 c. Accordingly, the processing unit of FIG. 5E illustratethat recurrent neural network 512 may include varying arrangements tothe placement of the inputs and outputs of delay units, as illustratedwith delay units 583 a, 583 b, and 583 c. For example, the output ofMLUs 588 b may be provided to delay unit 583 b, to generate a delayedversion of that processing result from the second layer of MAC units, asan input to the first layer of MAC units, e.g., as an input to MAC unit582 b. Accordingly, the recurrent neural network 512 of system 580 isillustrative that delayed versions of processing results may be providedas inputs to other hidden layers, different than the recurrent neuralnetwork 512 of system 550 in FIG. 5D showing respective delayed versionsbeing provided as inputs to the same layer in which those delayedversions were generated (e.g., the output of MLU 568 b is provided todelay unit 567 b, to generate a delayed version for the MAC unit 566 bin the same layer from which the processing result was outputted).Therefore, in the example, even the output B(n) 595 c may be provided,from the last hidden layer, to the first hidden layer (e.g., as an inputto MAC unit 582 c).

Advantageously, such delayed versions of processing results, which maybe provided as inputs to different or additional hidden layers, maybetter compensate “higher-order” memory effects in a recurrent neuralnetwork 170 that implements one or more recurrent neural network 512 ofFIG. 5E, e.g., as compared to the recurrent neural network 512 of FIG.5C or 5D. For example, higher-order memory effects model the effects ofleading and lagging envelope signals used during training of therecurrent neural network 170, to provide output data that estimates avector representative of self-interference noise between two differentantennas of an electronic device 110. In the example, a recurrent neuralnetwork 170 that estimates that vector (e.g., a Volterra series model)may include varying delayed versions of processing results thatcorresponds to such leading and lagging envelopes (e.g., of variousenvelopes encapsulating the vector). Accordingly, implementing therecurrent neural network 512 incorporates such higher-order memoryeffects, e.g., for an inference of a recurrent neural network 170, toprovide output data 595 a-c based on input data 581 a-c.

While described in FIGS. 5D and 5E respectively describe a recurrentneural network 512, it can be appreciated that the recurrent neuralnetwork 512 or combinations thereof may be implemented in or as any ofthe recurrent neural networks described herein, in operation to canceland/or compensate self-interference noise via the calculation of suchnoise as implemented in a recurrent neural network. In suchimplementations, recurrent neural networks may be used to reduce and/orimprove errors which may be introduced by self-interference noise.Advantageously, with such an implementation, wireless systems anddevices implementing such RNNs increase capacity of their respectivewireless networks because additional data may be transmitted in suchnetworks, which would not otherwise be transmitted due to the effects ofself-interference noise.

FIG. 6A is a schematic illustration 600 of an electronic device 610arranged in accordance with examples described herein. The electronicdevice 610 includes antennas 101, 103, 105, 107; wireless transmitters111, 113; power amplifiers 219, 229; wireless receivers 115, 117;compensation components 245, 247; and LNAs 249, 259, which may operatein a similar fashion as described with reference to FIG. 2A. Theelectronic device 610 also includes the recurrent neural network 640 andtraining unit 645 that may provide sample vectors 660 to the recurrentneural network 640. The recurrent neural network 170 may be utilized toimplement the recurrent neural network 640, for example. The trainingunit 645 may determine center vectors C_(i) and the connection weightsW_(ij), for example, by optimizing the minimized error of adjustedsignals (e.g., adjusted signals 508 y_(i)(n) of FIG. 5A). For example,an optimization problem can be solved utilizing a gradient descentprocedure that computes the error, such that the minimized error may beexpressed as:

E=Σ _(n=1) ^(M) ∥Y(n)−

∥²  (9)

may be a corresponding desired output vector. To solve this minimizationproblem, the training unit 645 may utilize sample vectors to determinethe center vectors C_(i) and the connection weights W_(ij).

To determine the center vectors C_(i), the training unit 645 may performa cluster analysis (e.g., a k-means cluster algorithm) to determine atleast one center vector among a corresponding set of vectors, such assample vectors 630 based on training points or random vectors. In thesample vector approach, a training point may be selected towards thecenter for each of the sample vectors 630. The training point may becenter of each cluster partition of a set of the sample vectors 630,such that optimizing the cluster center is expressed as minimized erroraway from the cluster center for a given training point in the clusterpartition. Such a relationship may be expressed as:

E _(k,means)=Σ_(j=1) ^(H)Σ_(n×1) ^(M) B _(jn) ∥X(n)−C _(j)∥²  (10)

where B_(jn) is the cluster partition or membership function forming anH×M matrix. Each column of H×M matrix represents an available samplevector and each row of H×M matrix represents a cluster. Each column mayinclude a single “1” in the row corresponding to the cluster nearest tothat training point and zeroes in the other entries of that column. Thetraining unit 645 may initialize the center of each cluster to adifferent randomly chosen training point. Then each training example maybe assigned by the training unit 645 to a processing element (e.g., aprocessing element 506) nearest to it. When all training points havebeen assigned by the training unit 645, the training unit 645 may findthe average position of the training point for each cluster and may movethe cluster center to that point, when the error away from the clustercenter for each training point is minimized, denoting the set of centervectors C; for the processing elements (e.g., the processing elements506).

To determine the connection weights W_(ij) for the connections betweenprocessing elements 506 and processing elements 509, the training unit645 may utilize a linear least-squares optimization according to aminimization of the weights expressed as:

$\begin{matrix}{{\min\limits_{W}{\sum\limits_{n = 1}^{M}{{{Y(n)} -}}^{2}}} = {\min\limits_{W}{{{WF} - \hat{Y}}}^{2}}} & (11)\end{matrix}$

where W={W_(ij)} is the L×H matrix of the connection weights, F is anH×M matrix comprising the outputs h_(i)(n) of the processing elements506, expressed in Equation 11.

may be a corresponding desired output matrix, with an L×M size.Accordingly, in matrix algebra form, connection weight matrix W may beexpressed as

$\begin{matrix}{W_{︷ = Y}F_{︷ = Y}^{+}{\lim\limits_{\alphaarrow 0}{F^{T}( {{FF}^{T} + {\alpha\; 1}} )}^{- 1}}} & (12)\end{matrix}$

where F⁺ is the pseudo-inverse of F.

In some examples, for example in the context of self-interferencecalculator 500 implemented as recurrent neural network 640, to determinethe connection weights W_(ij) for the connections between processingelements 506 and processing elements 509, a training unit 645 mayutilize a batch-processing embodiment where sample sets are readilyavailable (e.g., available to be retrieved from a memory). The trainingunit 645 may randomly initialize the connection weights in theconnection weight matrix W. The output vector Y(n) may be computed inaccordance with Equation 12. An error term e_(i)(n) may be computed foreach processing element 506, which may be expressed as:

e ^(i)(n)=y _(i)(n−

(n) (i=1,2, . . . ,L)  (13)

where

(n) is a corresponding desired output vector. The connection weights maybe adjusted in batch-processing examples in accordance with a machinelearning expression where a Y is the learning-rate parameter which couldbe fixed or time-varying. In the example, the machine learningexpression may be:

W _(ij)(n+1)=W _(ij)(n)+γe _(i)(n)f _(j)(∥X(n)−C _(i)∥) (i=1,2, . . .,L; j=1,2, . . . ,M)  (14)

Such a process may iterate until passing a specified error threshold. Inthe example, the total error may be expressed as:

ϵ=∥Y(n)−

∥²

Accordingly, the training unit 645 may iterate recursively the processdescribed herein until the error ϵ passes the specified error threshold,such as passing below the specified error threshold.

In some examples, when the training unit 645 is determining the centervectors C_(i) that are a non-linear set of vectors fitting a Gaussianfunction, a scaling factor σ may be used before determination of theconnection weights W_(ij) for the connections between processingelements 506 and processing elements 509 of a recurrent neural network640. In a Gaussian function example, a convex hull of the vectors C_(i)may be used such that the training points allow a smooth fit for theoutput of the processing elements 506. Accordingly, each center vectorC_(i) may be related to another center vector C_(i) of the processingelements 506, such that each center vector C_(i) activates anothercenter vector C_(i) when computing the connection weights. A scalingfactor may be based on heuristic that computes the P-nearest neighbor,such that:

$\begin{matrix}{\sigma_{i} = {\frac{1}{p}{\sum\limits_{j = 1}^{P}{{C_{j} - C_{i}}}^{2}}}} & ( {{i = 1},2,{\ldots\mspace{14mu}\ldots},H} )\end{matrix}$

where C_(j) (for i=1, 2, . . . , H) are the P-nearest neighbors ofC_(i).

FIG. 6B is a schematic illustration 650 of an electronic device 655arranged in accordance with examples described herein. The electronicdevice 655 includes antennas 101, 103, 105, 107; wireless transmitters111, 113; power amplifiers 219, 229; wireless receivers 115, 117;compensation components 265, 267; and LNAs 249, 259, which may operatein a similar fashion as described with reference to FIG. 2B. Theelectronic device 655 also includes the recurrent neural network 690 andtraining unit 685 that may provide sample vectors 680 to the recurrentneural network 690. The recurrent neural network 170 may be utilized toimplement the recurrent neural network 690, for example. The trainingunit 685 may determine center vectors C_(i) and the connection weightsW_(ij), for example, by optimizing the minimized error of adjustedsignals (e.g., adjusted signals 508 y_(i)(n) of FIG. 5A). In the samefashion as described with respect to FIG. 6A, the training unit 685 maydetermine such center vectors G and the connection weights W_(ij) forthe electronic device 655.

FIG. 7A is a schematic illustration of a full duplex compensation method700 in accordance with examples described herein. Example method 700 maybe implemented using, for example, electronic device 102, 110 of FIG. 1,electronic device 110 in FIG. 2A, electronic device 270 of FIG. 2B,electronic device 610 of FIG. 6A, electronic device 655 of FIG. 6B, orany system or combination of the systems depicted in the FIG. 1-2,5A-5E, or 6A-6B described herein. The operations described in blocks708-728 may also be stored as computer-executable instructions in acomputer-readable medium.

Example method 700 may begin with block 708 that starts execution of theself-interference compensation method and recites “determine vectors forrecurrent neural network.” In the example, the center vectors may bedetermined according a cluster analysis. For example, an error may beminimized such that the distance from the cluster center to a giventraining point is minimized. Block 708 may be followed by block 712 thatrecites “generate connection weights for a recurrent neural network.” Inthe example, the connection weights may be determined according to alinear least-squares optimization or a batch processing example asdescribed herein. Block 712 may be followed by block 716 that recites“receive recurrent neural network signals for transmission at recurrentneural network.” Amplified signals x₁(n), x₂(n), x₃(n), x_(N)(n) 502 maybe received as input to a recurrent neural network. In the example,transmitter output may be a stream of transmission data from acorresponding transmitter that is performing RF operations oncorresponding signals to be transmitted.

Block 716 may be followed by block 720 that recites “combine signals inaccordance with vectors and connection weights to generate adjustmentsignals based on self-interference noise.” For example, various ALUs,such as multiplication units, in an integrated circuit may be configuredto operate as the circuitry of FIG. 5A, thereby combining the amplifiedsignals x₁(n), x₂(n), x₃(n), x_(N)(n) 502 to generate adjusted signalsy₁(n), y₂(n), y₃(n), y_(L)(n) 508 as described herein. Block 720 may befollowed by a block 724 that recites “adjust signals received atrespective antennas with adjustment signals based on self-interferencenoise.” In the example, compensation components 245,247 may receive theadjusted signals y₁(n), y₂(n) 241, 243 and compensate an incomingreceived wireless transmission from antennas 105, 107. In the example,the compensation components 245, 247 may subtract the adjusted signalsy₁(n), y₂(n) 241, 243 from the received wireless transmission to producecompensated received signals for the respective wireless receivers 115,117, thereby achieving full duplex compensation mode. Block 724 may befollowed by block 728 that ends the example method 700.

In some examples, the blocks 708 and 712 may be an optional block. Forexample, determination of the center vectors and the connection weightsmay occur during a training mode of an electronic device describedherein, while the remaining blocks of method 700 may occur during anoperation mode of the electronic devices described herein.

FIG. 7B is a flowchart of a method 750 in accordance with examplesdescribed herein. Example method 750 may be implemented using, forexample, electronic device 102, 110 of FIG. 1, electronic device 110 inFIG. 2A, electronic device 270 of FIG. 2B, electronic device 610 of FIG.6A, electronic device 655 of FIG. 6B, or any system or combination ofthe systems depicted in the FIG. 1-2, 5A-5E, or 6A-6B described herein.The operations described in blocks 754-778 may also be stored ascomputer-executable instructions in a computer-readable medium such asthe mode configurable control 505, storing the executable instructions515.

Example method 750 may begin with a block 754 that starts execution ofthe mixing input data with coefficient data routine. The method mayinclude a block 758 recites “retrieving a plurality of coefficients froma memory.” As described herein, a memory look-up (MLU) units can beconfigured to retrieve a plurality of coefficients and provide theplurality of coefficients as the connection weights for a respectivelayer of processing elements of a recurrent neural network. For example,the memory may store (e.g., in a database) coefficients representativeof self-interference noise among various antennas of an electronicdevice. In the implementation of recurrent neural network 512 of FIG.5C, for example, the MLU 514 a can be a table look-up that retrieves oneor more coefficients (e.g., specific coefficients associated with afirst frequency) to be applied to both operands M and N, as well as anadditional coefficient to be applied to operand T. Accordingly, MLUs ofthe recurrent neural network may request the coefficients from a memorypart of the implementing computing device, from a memory part of anexternal computing device, or from a memory implemented in acloud-computing device. In turn, the plurality of coefficients may beretrieved from the memory as utilized by the recurrent neural network.

Block 758 may be followed by block 762 that recites “obtaining inputdata associated with a transmission to be processed at a recurrentneural network.” The input data may correspond to amplified signalsx₁(n), x₂(n) 221, 223 that is received as input data at a recurrentneural network 240 or any of the recurrent neural networks describedherein. Block 762 may be followed by block 766 that recites“calculating, at a first layer of multiplication/accumulation processingunits (MAC units) of the recurrent neural network, the input data anddelayed versions of respective outputs of the first layer of MAC unitswith the plurality of coefficients to generate first processingresults.” As described herein, the recurrent neural network utilizes theplurality of coefficients such that mixing the coefficients with inputdata and delayed versions of respective outputs of the first layer ofMAC units generates output data that reflects the processing of theinput data with coefficients by the circuitry of FIG. 5C, 5D, or 5E. Forexample, various ALUs in an integrated circuit may be configured tooperate as the circuitry of FIG. 5C, 5D, or 5E, thereby mixing the inputdata and delayed versions of respective outputs of the first layer ofMAC units with the coefficients as described herein. For example, withreference to FIG. 5C, the input data and delayed versions of respectiveoutputs of the first layer of MAC units may be calculated with theplurality of coefficients to generate first processing results, at afirst layer of multiplication/accumulation processing units (MAC units).In some examples, various hardware platforms may implement the circuitryof FIG. 5C, 5D, or 5E, such as an ASIC, a DSP implemented as part of aFPGA, or a system-on-chip.

Block 766 may be followed by block 770 that recites “calculating, atadditional layers of MAC units, the first processing results and delayedversions of at least a portion of the first processing results with theadditional plurality of coefficients to generate second processingresults.” As described herein, the recurrent neural network utilizesadditional plurality of coefficients such that mixing the coefficientswith certain processing results and delayed versions of at least aportion of those certain processing results generates output data thatreflects the processing of the input data with coefficients by thecircuitry of FIG. 5C, 5D, or 5E. For example, with reference to FIG. 5C,the processing results of the first layer (e.g., multiplicationprocessing results) and delayed versions of at least a portion of thoseprocessing results may be calculated with the additional plurality ofcoefficients to generate second processing results, at a second layer ofmultiplication/accumulation processing units (MAC units). The processingresults of the second layer may be calculated with an additionalplurality of coefficients to generate the output data B(1) 530.

Block 770 may be followed by block 774 that recites “providing, from therecurrent neural network, output data as adjustment signals tocompensation components.” As described herein, the output data may beprovided to compensation components 245, 247 to compensate or cancelself-interference noise. Once provided, received signals may also beadjusted based on the adjusted signals, such that both signals beingtransmitted and received are simultaneously being processed, therebyachieving full-duplex transmission. Block 774 may be followed by block778 that ends the example method 750. In some examples, the block 758may be an optional block.

The blocks included in the described example methods 700 and 750 are forillustration purposes. In some embodiments, these blocks may beperformed in a different order. In some other embodiments, variousblocks may be eliminated. In still other embodiments, various blocks maybe divided into additional blocks, supplemented with other blocks, orcombined together into fewer blocks. Other variations of these specificblocks are contemplated, including changes in the order of the blocks,changes in the content of the blocks being split or combined into otherblocks, etc.

FIG. 8 is a block diagram of an electronic device 800 arranged inaccordance with examples described herein. The electronic device 800 mayoperate in accordance with any example described herein, such aselectronic device 102, 110 of FIG. 1, electronic device 110 in FIG. 2A,electronic device 270 of FIG. 2B, electronic device 610 of FIG. 6A,electronic device 655 of FIG. 6B, or any system or combination of thesystems depicted in the Figures described herein. The electronic device800 may be implemented in a smartphone, a wearable electronic device, aserver, a computer, an appliance, a vehicle, or any type of electronicdevice. For example, FIGS. 9-12 describe various devices that may beimplemented as the electronic device 800 using a recurrent neuralnetwork 840 to generate inference results or train on data acquired fromthe sensor 830. Generally, recurrent neural networks (e.g., recurrentneural network 840) may make inference results based on data acquiredfrom the sensor 830. For example, in addition to using a RNN forcalculating or cancelling self-interference noise, an electronic device800 may use an RNN to make inference results regarding datasets acquiredvia a sensor 830 or via a data network 895. Making an inference resultsmay include determining a relationship among one or more datasets, amongone or more subsets of a dataset, or among different subsets of variousdatasets. For example, a determined relationship may be represented asan AI model that the RNN generates. Such a generated AI model may beutilized to make predictions about similar datasets or similar subsetsof a dataset, when provided as input to such an AI model. As one skilledin the art can appreciate, various types of inference results may bemade by a RNN, which could include generating one or more AI modelsregarding acquired datasets.

The sensor 830 included in the electronic device 800 may be any sensorconfigured to detect an environmental condition and quantify ameasurement parameter based on that environmental condition. Forexample, the sensor 830 may be a photodetector that detects light andquantifies an amount or intensity of light that is received or measuredat the sensor 830. A device with a sensor 830 may be referred to as asensor device. Examples of sensor devices include sensors for detectingenergy, heat, light, vibration, biological signals (e.g., pulse, EEG,EKG, heart rate, respiratory rate, blood pressure), distance, speed,acceleration, or combinations thereof. Sensor devices may be deployed onbuildings, individuals, and/or in other locations in the environment.The sensor devices may communicate with one another and with computingsystems which may aggregate and/or analyze the data provided from one ormultiple sensor devices in the environment.

The electronic device 800 includes a computing system 802, a recurrentneural network 840, an I/O interface 870, and a network interface 890coupled to a data network 895. The data network 895 may include anetwork of data devices or systems such as a data center or otherelectronic devices 800 that facilitate the acquisition of data sets orcommunicate inference results among devices coupled to the data network895. For example, the electronic device 800 may communicate inferenceresults about self-interference noise, which are calculated at therecurrent neural network 840, to communicate such inference results toother electronic devices 800 with a similar connection or to a datacenter where such inference results may be utilized as part of a dataset, e.g., for further training in a machine learning or AI system.Accordingly, in implementing the recurrent neural network 840 in theelectronic device 800, mobile and sensor devices that operate as theelectronic device 800 may facilitate a fast exchange of inferenceresults or large data sets that are communicated to data center fortraining or inference.

The computing system 802 includes a wireless transceiver 810. Thewireless transceiver may include a wireless transmitter and/or wirelessreceiver, such as wireless transmitter 300 and wireless receiver 400.Recurrent neural network 840 may include any type of microprocessor,central processing unit (CPU), an application specific integratedcircuits (ASIC), a digital signal processor (DSP) implemented as part ofa field-programmable gate array (FPGA), a system-on-chip (SoC), or otherhardware to provide processing for device 800.

The computing system 802 includes memory units 850 (e.g., memory look-upunit), which may be non-transitory hardware readable medium includinginstructions, respectively, for calculating self-interference noise orbe memory units for the retrieval, calculation, or storage of datasignals to be compensated or adjusted signals based on calculatedself-interference noise. The memory units 850 may be utilized to storedata sets from machine learning or AI techniques executed by theelectronic device 800. The memory units 850 may also be utilized tostore, determine, or acquire inference results for machine learning orAI techniques executed by the electronic device 800. In some examples,the memory units 850 may include one or more types of memory, includingbut not limited to: DRAM, SRAM, NAND, or 3D XPoint memory devices.

The computing system 802 may include control instructions that indicatewhen to execute such stored instructions for calculatingself-interference noise or for the retrieval or storage of data signalsto be compensated or adjusted signals based on calculatedself-interference noise. Upon receiving such control instructions, therecurrent neural network 840 may execute such control instructionsand/or executing such instructions with elements of computing system 802(e.g., wireless transceiver 810) to perform such instructions. Forexample, such instructions may include a program that executes themethod 700, a program that executes the method 750, or a program thatexecutes both methods 700 and 750. In some implementations, the controlinstructions include memory instructions for the memory units 850 tointeract with the recurrent neural network 840. For example, thecomputing system 802 may include an instruction that, when executed,facilitates the provision of a read request to the memory units 850 toaccess (e.g., read) a large dataset to determine an inference result. Asanother example, the control instructions may include an instructionthat, when executed, facilitates the provision of a write request to thememory units 850 to write a data set that the electronic device 800 hasacquired, e.g., via the sensor 830 or via the I/O interface 870. Controlinstructions may also include instructions for the memory units 850 tocommunicate data sets or inference results to a data center via datanetwork 895. For example, a control instruction may include aninstruction that memory units 850 write data sets or inference resultsabout self-interference noise, which are calculated at the recurrentneural network 840, to a cloud server at a data center where suchinference results may be utilized as part of a data set, e.g., forfurther training in a machine learning or AI system or for theprocessing of communications signals (e.g., to calculate or cancelself-interference noise).

Communications between the recurrent neural network 840, the I/Ointerface 870, and the network interface 890 are provided via aninternal bus 880. The recurrent neural network 840 may receive controlinstructions from the I/O interface 870 or the network interface 890,such as instructions to calculate or cancel self-interference noise).For example, the I/O interface 870 may facilitate a connection to acamera device that obtain images and communicates such images to theelectronic device 800 via the I/O interface 870.

Bus 880 may include one or more physical buses, communicationlines/interfaces, and/or point-to-point connections, such as PeripheralComponent Interconnect (PCI) bus, a Gen-Z switch, a CCIX interface, orthe like. The I/O interface 870 can include various user interfacesincluding video and/or audio interfaces for the user, such as a tabletdisplay with a microphone. Network interface 890 communications withother electronic devices, such as electronic device 800 or acloud-electronic server, over the data network 895. For example, thenetwork interface 890 may be a USB interface.

FIG. 9 illustrates an example of a wireless communications system 900 inaccordance with aspects of the present disclosure. The wirelesscommunications system 900 includes a base station 910, a mobile device915, a drone 917, a small cell 930, and vehicles 940, 945. The basestation 910 and small cell 930 may be connected to a network thatprovides access to the Internet and traditional communication links. Thesystem 900 may facilitate a wide-range of wireless communicationsconnections in a 5G system that may include various frequency bands,including but not limited to: a sub-6 GHz band (e.g., 700 MHzcommunication frequency), mid-range communication bands (e.g., 2.4 GHz),mmWave bands (e.g., 24 GHz), and a NR band (e.g., 3.5 GHz).

The wireless communication system 900 may be implemented as a 5Gwireless communication system, having various mobile and sensorendpoints. As an example, the vehicles 940, 945 may be mobile endpointsand the solar cells 937 may be sensor endpoints in the 5G wirelesscommunication system. Continuing in the example, the vehicles 940, 945and solar cells 937 may collect data sets used for training andinference of machine learning or AI techniques at those respectivedevices. Accordingly, the system 900 may facilitate the acquisition andcommunication of data sets or inference results for various devices inthe system 900 when implementing such recurrent neural networks asdescribed herein that enable faster training and inference, while alsoincreasing precision of inference results, e.g., including higher-ordermemory effects in a recurrent neural network 512 of FIGS. 5C-5E.Accordingly, the system 900 may include devices that cancelself-interference noise of one or more devices of the system 900 thatare transceiving on both the 4G or 5G bands, for example, such thatother devices that may be operating as 5G standalone transceiver systemscan communicate with such 4G/5G devices in an efficient and timelymanner.

Additionally or alternatively, the wireless communications connectionsmay support various modulation schemes, including but not limited to:filter bank multi-carrier (FBMC), the generalized frequency divisionmultiplexing (GFDM), universal filtered multi-carrier (UFMC)transmission, bi-orthogonal frequency division multiplexing (BFDM),sparse code multiple access (SCMA), non-orthogonal multiple access(NOMA), multi-user shared access (MUSA), and faster-than-Nyquist (FTN)signaling with time-frequency packing. Such frequency bands andmodulation techniques may be a part of a standards framework, such asLong Term Evolution (LTE) (e.g., 1.8 GHz band) or other technicalspecification published by an organization like 3GPP or IEEE, which mayinclude various specifications for subcarrier frequency ranges, a numberof subcarriers, uplink/downlink transmission speeds, TDD/FDD, and/orother aspects of wireless communication protocols.

The system 900 may depict aspects of a radio access network (RAN), andsystem 900 may be in communication with or include a core network (notshown). The core network may include one or more serving gateways,mobility management entities, home subscriber servers, and packet datagateways. The core network may facilitate user and control plane linksto mobile devices via the RAN, and it may be an interface to an externalnetwork (e.g., the Internet). Base stations 910, communication devices920, and small cells 930 may be coupled with the core network or withone another, or both, via wired or wireless backhaul links (e.g., SIinterface, X2 interface, etc.).

The system 900 may provide communication links connected to devices or“things,” such as sensor devices, e.g., solar cells 937, to provide anInternet of Things (“IoT”) framework. Connected things within the IoTmay operate within frequency bands licensed to and controlled bycellular network service providers, or such devices or things may. Suchfrequency bands and operation may be referred to as narrowband IoT(NB-IoT) because the frequency bands allocated for IoT operation may besmall or narrow relative to the overall system bandwidth. Frequencybands allocated for NB-IoT may have bandwidths of 1, 5, 10, or 20 MHz,for example.

Additionally or alternatively, the IoT may include devices or thingsoperating at different frequencies than traditional cellular technologyto facilitate use of the wireless spectrum. For example, an IoTframework may allow multiple devices in system 900 to operate at a sub-6GHz band or other industrial, scientific, and medical (ISM) radio bandswhere devices may operate on a shared spectrum for unlicensed uses. Thesub-6 GHz band may also be characterized as and may also becharacterized as an NB-IoT band. For example, in operating at lowfrequency ranges, devices providing sensor data for “things,” such assolar cells 937, may utilize less energy, resulting in power-efficiencyand may utilize less complex signaling frameworks, such that devices maytransmit asynchronously on that sub-6 GHz band. The sub-6 GHz band maysupport a wide variety of uses case, including the communication ofsensor data from various sensors devices.

In such a 5G framework, devices may perform functionalities performed bybase stations in other mobile networks (e.g., UMTS or LTE), such asforming a connection or managing mobility operations between nodes(e.g., handoff or reselection). For example, mobile device 915 mayreceive sensor data from the user utilizing the mobile device 915, suchas blood pressure data, and may transmit that sensor data on anarrowband IoT frequency band to base station 910. In such an example,some parameters for the determination by the mobile device 915 mayinclude availability of licensed spectrum, availability of unlicensedspectrum, and/or time-sensitive nature of sensor data. Continuing in theexample, mobile device 915 may transmit the blood pressure data becausea narrowband IoT band is available and can transmit the sensor dataquickly, identifying a time-sensitive component to the blood pressure(e.g., if the blood pressure measurement is dangerously high or low,such as systolic blood pressure is three standard deviations from norm).

Additionally or alternatively, mobile device 915 may formdevice-to-device (D2D) connections with other mobile devices or otherelements of the system 900. For example, the mobile device 915 may formRFID, WiFi, MultiFire, Bluetooth, or Zigbee connections with otherdevices, including communication device 920 or vehicle 945. In someexamples, D2D connections may be made using licensed spectrum bands, andsuch connections may be managed by a cellular network or serviceprovider. Accordingly, while the above example was described in thecontext of narrowband IoT, it can be appreciated that otherdevice-to-device connections may be utilized by mobile device 915 toprovide information (e.g., sensor data) collected on different frequencybands than a frequency band determined by mobile device 915 fortransmission of that information.

Moreover, some communication devices may facilitate ad-hoc networks, forexample, a network being formed with communication devices 920 attachedto stationary objects and the vehicles 940, 945, without a traditionalconnection to a base station 910 and/or a core network necessarily beingformed. Other stationary objects may be used to support communicationdevices 920, such as, but not limited to, trees, plants, posts,buildings, blimps, dirigibles, balloons, street signs, mailboxes, orcombinations thereof. In such a system 900, communication devices 920and small cell 930 (e.g., a small cell, femtocell, WLAN access point,cellular hotspot, etc.) may be mounted upon or adhered to anotherstructure, such as lampposts and buildings to facilitate the formationof ad-hoc networks and other IoT-based networks. Such networks mayoperate at different frequency bands than existing technologies, such asmobile device 915 communicating with base station 910 on a cellularcommunication band.

The communication devices 920 may form wireless networks, operating ineither a hierarchal or ad-hoc network fashion, depending, in part, onthe connection to another element of the system 900. For example, thecommunication devices 920 may utilize a 700 MHz communication frequencyto form a connection with the mobile device 915 in an unlicensedspectrum, while utilizing a licensed spectrum communication frequency toform another connection with the vehicle 945. Communication devices 920may communicate with vehicle 945 on a licensed spectrum to providedirect access for time-sensitive data, for example, data for anautonomous driving capability of the vehicle 945 on a 5.9 GHz band ofDedicated Short Range Communications (DSRC).

Vehicles 940 and 945 may form an ad-hoc network at a different frequencyband than the connection between the communication device 920 and thevehicle 945. For example, for a high bandwidth connection to providetime-sensitive data between vehicles 940, 945, a 24 GHz mmWave band maybe utilized for transmissions of data between vehicles 940, 945. Forexample, vehicles 940, 945 may share real-time directional andnavigation data with each other over the connection while the vehicles940, 945 pass each other across a narrow intersection line. Each vehicle940, 945 may be tracking the intersection line and providing image datato an image processing algorithm to facilitate autonomous navigation ofeach vehicle while each travels along the intersection line. In someexamples, this real-time data may also be substantially simultaneouslyshared over an exclusive, licensed spectrum connection between thecommunication device 920 and the vehicle 945, for example, forprocessing of image data received at both vehicle 945 and vehicle 940,as transmitted by the vehicle 940 to vehicle 945 over the 24 GHz mmWaveband. While shown as automobiles in FIG. 9, other vehicles may be usedincluding, but not limited to, aircraft, spacecraft, balloons, blimps,dirigibles, trains, submarines, boats, ferries, cruise ships,helicopters, motorcycles, bicycles, drones, or combinations thereof.

While described in the context of a 24 GHz mmWave band, it can beappreciated that connections may be formed in the system 900 in othermmWave bands or other frequency bands, such as 28 GHz, 37 GHz, 38 GHz,39 GHz, which may be licensed or unlicensed bands. In some cases,vehicles 940, 945 may share the frequency band that they arecommunicating on with other vehicles in a different network. Forexample, a fleet of vehicles may pass vehicle 940 and, temporarily,share the 24 GHz mmWave band to form connections among that fleet, inaddition to the 24 GHz mmWave connection between vehicles 940, 945. Asanother example, communication device 920 may substantiallysimultaneously maintain a 700 MHz connection with the mobile device 915operated by a user (e.g., a pedestrian walking along the street) toprovide information regarding a location of the user to the vehicle 945over the 5.9 GHz band. In providing such information, communicationdevice 920 may leverage antenna diversity schemes as part of a massiveMIMO framework to facilitate time-sensitive, separate connections withboth the mobile device 915 and the vehicle 945. A massive MIMO frameworkmay involve a transmitting and/or receiving devices with a large numberof antennas (e.g., 12, 20, 64, 128, etc.), which may facilitate precisebeamforming or spatial diversity unattainable with devices operatingwith fewer antennas according to legacy protocols (e.g., WiFi or LTE).

The base station 910 and small cell 930 may wirelessly communicate withdevices in the system 900 or other communication-capable devices in thesystem 900 having at the least a sensor wireless network, such as solarcells 937 that may operate on an active/sleep cycle, and/or one or moreother sensor devices. The base station 910 may provide wirelesscommunications coverage for devices that enter its coverages area, suchas the mobile device 915 and the drone 917. The small cell 930 mayprovide wireless communications coverage for devices that enter itscoverage area, such as near the building that the small cell 930 ismounted upon, such as vehicle 945 and drone 917.

Generally, a small cell 930 may be referred to as a small cell andprovide coverage for a local geographic region, for example, coverage of200 meters or less in some examples. This may contrasted with atmacrocell, which may provide coverage over a wide or large area on theorder of several square miles or kilometers. In some examples, a smallcell 930 may be deployed (e.g., mounted on a building) within somecoverage areas of a base station 910 (e.g., a macrocell) where wirelesscommunications traffic may be dense according to a traffic analysis ofthat coverage area. For example, a small cell 930 may be deployed on thebuilding in FIG. 9 in the coverage area of the base station 910 if thebase station 910 generally receives and/or transmits a higher amount ofwireless communication transmissions than other coverage areas of thatbase station 910. Abase station 910 may be deployed in a geographic areato provide wireless coverage for portions of that geographic area. Aswireless communications traffic becomes more dense, additional basestations 910 may be deployed in certain areas, which may alter thecoverage area of an existing base station 910, or other support stationsmay be deployed, such as a small cell 930. Small cell 930 may be afemtocell, which may provide coverage for an area smaller than a smallcell (e.g., 100 meters or less in some examples (e.g., one story of abuilding)).

While base station 910 and small cell 930 may provide communicationcoverage for a portion of the geographical area surrounding theirrespective areas, both may change aspects of their coverage tofacilitate faster wireless connections for certain devices. For example,the small cell 930 may primarily provide coverage for devicessurrounding or in the building upon which the small cell 930 is mounted.However, the small cell 930 may also detect that a device has entered iscoverage area and adjust its coverage area to facilitate a fasterconnection to that device.

For example, a small cell 930 may support a massive MIMO connection withthe drone 917, which may also be referred to as an unmanned aerialvehicle (UAV), and, when the vehicle 945 enters it coverage area, thesmall cell 930 adjusts some antennas to point directionally in adirection of the vehicle 945, rather than the drone 917, to facilitate amassive MIMO connection with the vehicle, in addition to the drone 917.In adjusting some of the antennas, the small cell 930 may not support asfast as a connection to the drone 917 at a certain frequency, as it hadbefore the adjustment. For example, the small cell 930 may becommunicating with the drone 917 on a first frequency of variouspossible frequencies in a 4G LTE band of 1.8 GHz. However, the drone 917may also request a connection at a different frequency with anotherdevice (e.g., base station 910) in its coverage area that may facilitatea similar connection as described with reference to the small cell 930,or a different (e.g., faster, more reliable) connection with the basestation 910, for example, at a 3.5 GHz frequency in the 5G NR band.Accordingly, the system 900 may enhance existing communication links inproviding additional connections to devices that may utilize or demandsuch links, while also compensating for any self-interference noisegenerated by the drone 917 in transmitting, for example, in both the 4GELTE and 5G NR bands. In some examples, drone 917 may serve as a movableor aerial base station.

The wireless communications system 900 may include devices such as basestation 910, communication device 920, and small cell 930 that maysupport several connections at varying frequencies to devices in thesystem 900, while also compensating for self-interference noiseutilizing recurrent neural networks, such as recurrent neural network170. Such devices may operate in a hierarchal mode or an ad-hoc modewith other devices in the network of system 900. While described in thecontext of a base station 910, communication device 920, and small cell930, it can be appreciated that other devices that can support severalconnections with devices in the network, while also compensating forself-interference noise utilizing recurrent neural networks, may beincluded in system 900, including but not limited to: macrocells,femtocells, routers, satellites, and RFID detectors.

In various examples, the elements of wireless communication system 900,such as base station 910, a mobile device 915, a drone 917,communication device 920 a small cell 930, and vehicles 940, 945, may beimplemented as an electronic device described herein that compensate forself-interference noise utilizing recurrent neural networks. Forexample, the communication device 920 may be implemented as electronicdevices described herein, such as electronic device 102, 110 of FIG. 1,electronic device 110 in FIG. 2A, electronic device 270 of FIG. 2B,electronic device 610 of FIG. 6A, electronic device 655 of FIG. 6B,electronic device 800 of FIG. 8, or any system or combination of thesystems depicted in the FIG. 1-2, 5A-5E, 6A-6B, or 8 described herein.Accordingly, any of the devices of system 900 may transceive signals onboth 4G and 5G bands; while also compensating for self-interferencenoise utilizing recurrent neural networks.

FIG. 10 illustrates an example of a wireless communications system 1000in accordance with aspects of the present disclosure. The wirelesscommunications system 1000 includes a mobile device 1015, a drone 1017,a communication device 1020, and a small cell 1030. A building 1010 alsoincludes devices of the wireless communication system 1000 that may beconfigured to communicate with other elements in the building 1010 orthe small cell 1030. The building 1010 includes networked workstations1040, 1045, virtual reality device 1050, IoT devices 1055, 1060, andnetworked entertainment device 1065. In the depicted system 1000, IoTdevices 1055, 1060 may be a washer and dryer, respectively, forresidential use, being controlled by the virtual reality device 1050.Accordingly, while the user of the virtual reality device 1050 may be indifferent room of the building 1010, the user may control an operationof the IoT device 1055, such as configuring a washing machine setting.Virtual reality device 1050 may also control the networked entertainmentdevice 1065. For example, virtual reality device 1050 may broadcast avirtual game being played by a user of the virtual reality device 1050onto a display of the networked entertainment device 1065.

The small cell 1030 or any of the devices of building 1010 may beconnected to a network that provides access to the Internet andtraditional communication links. Like the system 900, the system 1000may facilitate a wide-range of wireless communications connections in a5G system that may include various frequency bands, including but notlimited to: a sub-6 GHz band (e.g., 700 MHz communication frequency),mid-range communication bands (e.g., 2.4 GHz), and mmWave bands (e.g.,24 GHz). Additionally or alternatively, the wireless communicationsconnections may support various modulation schemes as described abovewith reference to system 900. System 1000 may operate and be configuredto communicate analogously to system 900. Accordingly, similarlynumbered elements of system 1000 and system 900 may be configured in ananalogous way, such as communication device 920 to communication device1020, small cell 930 to small cell 1030, etc.

Like the system 900, where elements of system 900 are configured to formindependent hierarchal or ad-hoc networks, communication device 1020 mayform a hierarchal network with small cell 1030 and mobile device 1015,while an additional ad-hoc network may be formed among the small cell1030 network that includes drone 1017 and some of the devices of thebuilding 1010, such as networked workstations 1040, 1045 and IoT devices1055, 1060.

Devices in communication system 1000 may also form (D2D) connectionswith other mobile devices or other elements of the system 1000. Forexample, the virtual reality device 1050 may form a narrowband IoTconnections with other devices, including IoT device 1055 and networkedentertainment device 1065. As described above, in some examples, D2Dconnections may be made using licensed spectrum bands, and suchconnections may be managed by a cellular network or service provider.Accordingly, while the above example was described in the context of anarrowband IoT, it can be appreciated that other device-to-deviceconnections may be utilized by virtual reality device 1050.

In various examples, the elements of wireless communication system 1000,such as the mobile device 1015, the drone 1017, the communication device1020, and the small cell 1030, the networked workstations 1040, 1045,the virtual reality device 1050, the IoT devices 1055, 1060, and thenetworked entertainment device 1065, may be implemented as electronicdevices described herein that compensate for self-interference noiseutilizing recurrent neural networks. For example, the communicationdevice 1020 may be implemented as electronic devices described herein,such as electronic device 102, 110 of FIG. 1, electronic device 110 inFIG. 2A, electronic device 270 of FIG. 2B, electronic device 610 of FIG.6A, electronic device 655 of FIG. 6B, electronic device 800 of FIG. 8,or any system or combination of the systems depicted in the FIG. 1-2,5A-5E, 6A-6B, or 8 described herein. Accordingly, any of the devices ofsystem 1000 may transceive signals on both 4G and 5G bands; while alsocompensating for self-interference noise utilizing recurrent neuralnetworks.

The wireless communication system 1000 may also be implemented as a 5Gwireless communication system, having various mobile or other electronicdevice endpoints. As an example, the mobile devices 1015 may be mobileendpoints and the networked entertainment device 1065 may include acamera (e.g., via an I/O interface 870) in the 5G wireless communicationsystem. Continuing in the example, mobile device 1015 may collect datasets used for training and inference of machine learning or AItechniques at that respective device, e.g., a direction of travel to thebuilding 1010 where networked entertainment device 1065 is located.Using such data sets in AI technique, the wireless communication system1000 may determine inference results, such as a prediction of the userof mobile device 1015 to arrive at the building 1010 where the networkedentertainment device 1065 is located. Moreover, the networkedentertainment device 1065 may acquire images of users (e.g., as adataset) interacting with the networked entertainment device 1065 todetermine inference results about the content displayed on networkedentertainment device 1065. For example, an inference result may be thatthe users interacting with the networked entertainment device 1065 wouldlike to interact with additional similar content. Using both inferenceresults, the system 1000 can facilitate predictions about users of oneor more devices in system 1000. Such a combined, inference result mayinclude a subset of the inference results of each respective device.Continuing in the example, either the mobile device 1015 or networkedentertainment device 1065 can determine a combined, inference results,such as that the user to arrive at the building 1010 where the networkedentertainment device 1065 is located may be interested in interactingwith additional similar content to that of users of networkedentertainment device 1065 interacting with certain content displayed onnetworked entertainment device 1065.

Therefore, the system 1000 may facilitate the acquisition andcommunication of data sets or inference results for various devices inthe system 1000 when implementing such recurrent neural networks asdescribed herein that enable faster training and inference, while alsoincreasing precision of inference results, e.g., including higher-ordermemory effects in a recurrent neural network 512 of FIGS. 5C-5E.Accordingly, the system 1000 may include devices that cancelself-interference noise of one or more devices of the system 1000 thatare transceiving on both the 4G or 5G bands, for example, such thatother devices that may be operating as 5G standalone transceiver systemscan communicate with such 4G/5G devices in an efficient and timelymanner. In various implementations of system 1000, the devices displayedon or in the building 1010 may be referred to as “smart home” 5G devicesthat acquire data sets and determine inference results regarding contentor users interacting with those devices. For example, the “smart home”5G devices may include the networked workstations 1040, 1045, virtualreality device 1050, IoT devices 1055, 1060, and networked entertainmentdevice 1065.

FIG. 11 illustrates an example of a wireless communication system 1100in accordance with aspects of the present disclosure. The wirelesscommunication system 1100 includes small cells 1110, camera devices1115, communication devices 1120, sensor devices 1130, communicationdevices 1140, data center 1150, and sensor device 1160. In the depictedsystem 1100, a small cell 1110 may form a hierarchal network, for anagricultural use, with a camera device 1115, communication devices 1120,sensor devices 1130, and sensor device 1160. Such a network formed bysmall cell 1110 may communicate data sets and inference results amongthe networked devices and the data center 1150. Continuing in thedepicted system 1100, another small cell 1110 may form anotherhierarchal network, for another agricultural use, with communicationdevices 1140, data center 1150, and sensor device 1160. Similarly, sucha network formed by the additional small cell 1110 may communicate datasets and inference results among the networked devices and the datacenter 1150. While depicted in certain agricultural networks withparticular small cells 1110, it can be appreciated that variousnetworks, whether hierarchal or ad-hoc, may be formed among the devices,cells, or data center of wireless communication system 1100.Additionally or alternatively, like the system 900 or system 1000, itcan be appreciated that similarly-named elements of system 1100 may beconfigured in an analogous way, such as communication device 920 tocommunication device 1120, communication device 1020 to communicationdevice 1140, or small cell 930 to small cell 1110, etc.

Like the system 1000, devices in system 1100 may also D2D connectionswith other mobile devices or other elements of the system 1000. Forexample, the communication device 1140 may form a narrowband IoTconnections with other devices, including sensor device 1160 orcommunication device 1120. As described above, in some examples, D2Dconnections may be made using licensed spectrum bands, and suchconnections may be managed by a cellular network or service provider,e.g., a cellular network or service provider of small cell 1110.Accordingly, while the above example was described in the context of anarrowband IoT, it can be appreciated that other device-to-deviceconnections may be utilized by the devices of system 1100.

In various examples, the elements of wireless communication system 1100,such as the camera device 1115, communication devices 1120, sensordevices 1130, communication devices 1140, sensor device 1160, may beimplemented as electronic devices described herein that compensate forself-interference noise utilizing recurrent neural networks. Forexample, the sensor device 1160 may be implemented as electronic devicesdescribed herein, such as electronic device 102, 110 of FIG. 1,electronic device 110 in FIG. 2A, electronic device 270 of FIG. 2B,electronic device 610 of FIG. 6A, electronic device 655 of FIG. 6B,electronic device 800 of FIG. 8, or any system or combination of thesystems depicted in the FIG. 1-2, 5A-5E, 6A-6B, or 8 described herein.Accordingly, any of the devices of system 1100 may transceive signals onboth 4G and 5G bands; while also compensating for self-interferencenoise utilizing recurrent neural networks.

The wireless communication system 1100 may also be implemented as a 5Gwireless communication system, having various mobile or other electronicdevice endpoints. As an example, the camera device 1115, including acamera (e.g., via an I/O interface 870) may be a mobile endpoint; andcommunication devices 1120 and sensor devices 1130 may be sensorendpoints in a 5G wireless communication system. Continuing in theexample, the camera device 1115 may collect data sets used for trainingand inference of machine learning or AI techniques at that respectivedevice, e.g., images of watermelons in an agricultural field. Such imagedata sets may be acquired by the camera device and stored in a memory ofthe camera device 1115 (e.g., such as memory units 850) to communicatethe data sets to the data center 1150 for training or processinginference results based on the image data sets. Using such data sets inan AI technique, the wireless communication system 1100 may determineinference results, such as a prediction of a growth rate of thewatermelons based on various stages of growth for different watermelonsin the agricultural field, e.g., a full-grown watermelon or anintermediate growth of a watermelon as indicated by a watermelon floweron the watermelon. For example, the data center 1150 may make inferenceresults using a recurrent neural network 840 on a cloud serverimplementing the electronic device 800, to provide such inferenceresults for use in the wireless communication system 1100.

Continuing in the example of the 5G communication system processinginference results with mobile and/or sensor endpoints, the communicationdevices 1120 may communicate data sets to the data center 1150 via thesmall cell 1110. For example, the communication devices 1120 may acquiredata sets about a parameter of the soil of the agricultural field inwhich the watermelons are growing via a sensor included on therespective communication device 1120 (e.g., a sensor 830). Suchparameterized data sets may also be stored at the communication device1120 or communicated to the data center 1150 for further training orprocessing of inference results using ML or AI techniques. While astatus of the agricultural field has been described with respect theexample of acquiring data sets about a parameter of the field, it can beappreciated that various data sets about a status of the agriculturalfield can be acquired, depending on sensors utilized by a communicationdevice 1120 to measure parameters of the agricultural field.

In some implementations, the communication device 1120 may makeinference results at the communication device 1120 itself. In suchimplementations, the communication device 1120 may acquire certainparameterized data sets regarding the soil and make inference resultsusing a recurrent neural network 840 on the communication device 1120itself. The inference result may be a recommendation regarding an amountof water for the soil of the agricultural field. Based on such aninference result, the communication device 1120 may be furtherconfigured to communicate that inference result to another device alongwith a control instruction for that device. Continuing in the example,the communication device 1120 may obtain such an inference result andgenerate an instruction for sensor device 1130 to increase or decreasean amount of water according to the inference result. Accordingly,inference results may be processed at devices of system 1100 or at thedata center 1150 based on data sets acquired by the various devices ofsystem 1100.

Continuing in the example of the 5G communication system processinginference results with mobile and/or sensor endpoints, the sensor device1130 may communicate data sets to the data center 1150 via the smallcell 1110. For example, the sensor device 1130 may acquire data setsabout water usage in the agricultural field in which the watermelons aregrowing because the sensor device is sprinkler implemented as anelectronic device as described herein, such as electronic device 800.For example, the sensor device 1130 may include a sensor 830 thatmeasures water usage, such as a water gauge. Accordingly, the sensordevice 1130 may acquire a data set regarding the water usage to bestored at memory units 850 or communicated to a data center 1150 forprocessing of inference results. Such parameterized data sets may alsobe stored at the communication device 1120 or communicated to the datacenter 1150 for further training or processing of inference resultsusing ML or AI techniques. While a status of water usage has beendescribed with respect to the example of a sprinkler and a water gauge,it can be appreciated that various data sets about a status of the waterusage can be acquired, depending on sensors utilized by a sensor device1120 to acquire data sets about water usage.

Additionally or alternatively in the example of the 5G communicationsystem processing inference results, the sensor device 1160 maycommunicate data sets or make inference results for use by one of theother devices of the system 1100 or the data center 1150. For example,the sensor device 1160 can acquire a dataset regarding a windspeed orother environmental condition of the agricultural setting of system1100. In the example, the sensor device 1160 may implement theelectronic device having a sensor 830 as an anemometer. Accordingly, inthe example the sensor 830 may acquire a windspeed and store a data setregarding that wind speed in one or more memory units 850. In someimplementations, the sensor device 1160 may utilize the recurrent neuralnetwork 840 to make inference results regarding the data set stored inthe memory units 850. For example, an inference result may be that acertain wind speed in a particular direction is indicative ofprecipitation. Accordingly, that inference results may be communicatedin a 5G transmission, including while other signals are communicated ona 4G band at the sensor device 1160 or small cell 1110, to the smallcell 1110.

In some implementations, the small cell 1110 may further route such aninference result to various devices or the data center 1150. Continuingin the example, the inference result from the sensor device 1160 may beutilized by the sensor devices 1130 to adjust a water usage in theagricultural field. In such a case, the sensor devices 1130 may processthe inference results from the sensor device 1130 at a recurrent neuralnetwork(s) 840 of the respective sensor devices 1130 to adjust the waterusage in the agricultural field growing watermelons based on theinference result that a certain wind speed in a particular direction isindicative of precipitation. Accordingly, advantageously, the system1100, in acquiring data sets and processing inference results atrespective recurrent neural networks 840, may provide a sustainabilityadvantage in conserving certain natural resources that the devices ofsystem 1100 may interact with, such as the sensor devices 1130interacting with a water natural resource. Using such inference results,the system 1100 can facilitate predictions about natural resourcesutilized by the agricultural field devices in system 1100.

As another example of the wireless communication systems 1100implementing a 5G wireless communication system, having various mobileor other electronic device endpoints, another camera device 1115,including a camera (e.g., via an I/O interface 870) and communicationdevices 1140 attached to certain agricultural livestock (e.g., cows asdepicted in FIG. 11) may be additional mobile endpoints; and sensordevice 1160 may be a sensor endpoint in a 5G wireless communicationsystem. In the example, the communication devices 1140 may beimplemented as certain narrowband IoT devices that may utilize lessenergy, resulting in power-efficiency and may utilize less complexsignaling frameworks, such that devices may transmit asynchronously onparticular bands. The I/O interface 870 of the communication devices maybe coupled to a Global Positioning System (GPS) device that provides alocation of the communication devices 1140 attached to certainagricultural livestock. The respective communication devices 1140 mayacquire respective location data sets that track the movement of thelivestock in an agricultural field. Advantageously, the communicationdevices 1140 may provide such data sets to other devices in the system1100, such as the data center 1150, for further processing of inferenceresults regarding the respective location data sets. Such data sets maybe communicated, from the respective communication devices 1140, in a 5Gtransmission; while other signals are communicated on a 4G band at thecommunication devices 1140 or small cell 1110, to the small cell 1110.

Continuing in the example, the camera device 1115 may also acquireimages of the livestock with the communication devices 1140 attachedthereto, for further processing of inference results with the respectivelocation datasets. In an example, the image data sets acquired by thecamera device 1115 and the location data sets acquired by thecommunication devices 1140 may be communicated to the data center 1150via the small cell 1110. Accordingly, the data center 1150 may make aninference result based on the image and location data sets. For example,a recurrent neural network 840 on a cloud server, implemented aselectronic device 800 at the data center 1150, may make an inferenceresult that predicts when the livestock are to be removed from theagricultural field due to a consumption of a natural resource. Theinference result may be based on the condition of the agricultural fieldbased on images from the image data set and the temporal location of thelivestock indicative of how long the livestock have consumed aparticular natural resource (e.g., grass) based on the location dataset. In some examples, the cloud server at the data center 1150 (oranother cloud server at the data center 1150) may further process thatinference result with an additional data set, such as a data setregarding the wind speed acquired by the sensor device 1160, to furtherprocess that inference result with an inference result regarding aprecipitation prediction based on a certain wind speed in a particulardirection. Accordingly, multiple inference results may be processed atthe data center 1150 based on various data sets that the devices ofsystem 1100 acquire.

Therefore, the system 1100 may facilitate the acquisition andcommunication of data sets or inference results for various devices inthe system 1100 when implementing such recurrent neural networks asdescribed herein that enable high-capacity training and inference, whilealso increasing precision of inference results, e.g., includinghigher-order memory effects in a recurrent neural network 512 of FIGS.5C-5E. For example, various devices of system 1100 may facilitateprocessing of data acquired, e.g., to efficiently offload such data todata center 1150 for AI or machine learning (ML) techniques to beapplied. Accordingly, the system 1100 may include devices that cancelself-interference noise of one or more devices of the system 1100 thatare transceiving on both the 4G or 5G bands, for example, such thatother devices that may be operating as 5G standalone transceiver systemscan communicate with such 4G/5G devices in an efficient and timelymanner.

FIG. 12 illustrates an example of a communication system 1200 inaccordance with aspects of the present disclosure. The communicationsystem 1200 includes small cells 1210, wired communication link 1212,drone 1217, industrial user 1220, industrial communication device 1227,substation 1230, industrial pipeline 1225, pipeline receiving station1235, pipeline communication device 1237, residential user 1240,commercial user 1245, data center 1250, sensor device 1255, powergeneration user 1260, fuel station 1270, substation 1275, and fuelstorage 1280.

In the depicted communication system 1200, small cells 1210 may form ahierarchal network to provide a status of the fuel for various users ofthe industrial pipeline system, thereby facilitating fuel transmission,distribution, storage, or power generation based on distributed fuel.The fuel may be various types of gas or oil, for example, crude oil,diesel gas, hydrogen gas, or natural gas. The fuel may be provided andutilized by an industrial user 1220, substation 1230 or substation 1275,residential user 1240, commercial user 1245, or fuel station 1270.Various statuses regarding the fuel may be provided to small cells 1210,drone 1217, data center 1250, or wired communication link 1212 by thevarious communication devices in such an industrial communication system1200. For example, industrial communication device 1227, pipelinecommunication device 1237, or sensor device 1255 may provide a status asto a flow of the fuel through the pipeline network depicted in FIG. 12.Additionally or alternatively, the fuel may be provided through thepipeline network for use in power generation at power generation user1260 or for storage at fuel storage 1280. The fuel is provided to thepipeline network by industrial pipeline 1225 at pipeline receivingstation 1235.

As fuel flows through the pipeline network, industrial communicationdevice 1227, pipeline communication device 1237, or sensor device 1255may be implemented as electronic devices 800 with sensors 830 or I/Ointerfaces 870 coupled to various I/O devices to receive data input asto a status of the fuel. Accordingly, data sets regarding the fuel maybe acquired by the industrial communication device 1227, pipelinecommunication device 1237, or sensor device 1255 for further processingof inference results regarding a status of the fuel. For example, thepipeline communication device 1237 may communicate via a 5Gcommunications signal a status indicative of power consumption atvarious users of the pipeline network, such as industrial user 1220,residential user 1240, or commercial user 1245. As another example,substation 1230 or substation 1275 may provide a power generation statusas to power generated by elements of pipeline network coupled to thesubstations 1230 or substation 1275. Accordingly, substation 1230 mayprovide a power generation status of industrial user 1220; andsubstation 1275 may provide a power generation status as powergeneration user 1260. Such various statuses may be provided to the datacenter 1250 via drone 1217 or small cells 1210 communicating withdevices located at the respective users of the pipeline network ordevices located at the substations 1230 or 1275. In the implementationof system 1200, a fuel storage status may also be provided to the datacenter 1250 by the fuel storage 1280.

While system 1200 is depicted in a particular pipeline network system,it can be appreciated that various networks, whether hierarchal orad-hoc, may be formed among the devices, cells, or data center 1250 ofwireless communication system 1200. Additionally or alternatively, likethe system 900, system 1000, system 1100, it can be appreciated thatsimilarly-named elements of system 1200 may be configured in ananalogous way, such as communication device 920 to pipelinecommunication device 1237, drone 917 to drone 1217, or small cell 930 tosmall cell 1210, etc.

Like the system 1100, devices in system 1200 may also D2D connectionswith other mobile devices or other elements of the system 1200. Forexample, the pipeline communication device 1237 may form a narrowbandIoT connections with other devices, including industrial communicationdevice 1227 or sensor device 1255. As described above, in some examples,D2D connections may be made using licensed spectrum bands, and suchconnections may be managed by a cellular network or service provider,e.g., a cellular network or service provider of small cell 1210.Accordingly, while the above example was described in the context of anarrowband IoT, it can be appreciated that other device-to-deviceconnections may be utilized by the devices of system 1200.

In various examples, the elements of wireless communication system 1200,such as the industrial communication device 1227, pipeline communicationdevice 1237, or sensor device 1255, may be implemented as electronicdevices described herein that compensate for self-interference noiseutilizing recurrent neural networks. For example, the sensor device 1255may be implemented as electronic devices described herein, such aselectronic device 102, 110 of FIG. 1, electronic device 110 in FIG. 2A,electronic device 270 of FIG. 2B, electronic device 610 of FIG. 6A,electronic device 655 of FIG. 6B, electronic device 800 of FIG. 8, orany system or combination of the systems depicted in the FIG. 1-2,5A-5E, 6A-6B, or 8 described herein. Accordingly, any of the devices ofsystem 1200 may transceive signals on both 4G and 5G bands; while alsocompensating for self-interference noise utilizing recurrent neuralnetworks.

In an example of processing industrially-acquired data sets of thesystem 1200, the devices of system 1200, such as industrialcommunication device 1227, pipeline communication device 1237, or sensordevice 1255, and users of system 1200 may communicate, via communicated5G signals, data sets regarding a status of the fuel, power consumption,or power generation, to the data center 1250 for further processing ofinference results. In an example, a fuel flow status at sensor device1255 may be communicated to the data center 1250 via the small cell1210. As another example, a power consumption status of residential user1240 may be communicated via a 5G communications signal to the datacenter 1250 via small cell 1210 or drone 1217. As yet another example, apower generation status may be communicated via a 5G communicationssignal to the small cell 1210 from power generation user 1260, and thenfurther communicated to the data center 1250 via a wired communicationlink 1212. Accordingly, the data center 1250 may acquire data sets fromvarious communication devices or users of system 1200. The data centermay process one or more inference results based on such acquired datasets. For example, a recurrent neural network 840 on a cloud server,implemented as electronic device 800 at the data center 1250, may makean inference result that predicts when a fuel shortage or surplus mayoccur based on a power consumption status at various users of the system1200 and a fuel flow status received from pipeline communication device1237 detecting a fuel flow from pipeline 1225 via pipeline receivingstation 1235. In some examples, the cloud server at the data center 1250(or another cloud server at the data center 1250) may further processthat inference result with one or more additional data sets, to furtherprocess that inference result with one or more other inference results.Accordingly, multiple inference results may be processed at the datacenter 1250 based on various data sets that the devices of system 1200acquire. Therefore, the devices of system 1200 may facilitate processingof data acquired, e.g., to efficiently offload such data to data center1250 for AI or machine learning (ML) techniques to be applied.

Accordingly, the system 1200 may facilitate the acquisition andcommunication of data sets or inference results for various devices inthe system 1200 when implementing such recurrent neural networks asdescribed herein that enable higher-capacity training and inference,while also increasing precision of inference results, e.g., includinghigher-order memory effects in a recurrent neural network 512 of FIGS.5C-5E. Accordingly, the system 1200 may include devices that cancelself-interference noise of one or more devices of the system 1200 thatare transceiving on both the 4G or 5G bands, for example, such thatother devices that may be operating as 5G standalone transceiver systemscan communicate with such 4G/5G devices in an efficient and timelymanner.

Certain details are set forth above to provide a sufficientunderstanding of described examples. However, it will be clear to oneskilled in the art that examples may be practiced without various ofthese particular details. The description herein, in connection with theappended drawings, describes example configurations and does notrepresent all the examples that may be implemented or that are withinthe scope of the claims. The terms “exemplary” and “example” as may beused herein means “serving as an example, instance, or illustration,”and not “preferred” or “advantageous over other examples.” The detaileddescription includes specific details for the purpose of providing anunderstanding of the described techniques. These techniques, however,may be practiced without these specific details. In some instances,well-known structures and devices are shown in block diagram form inorder to avoid obscuring the concepts of the described examples.

Information and signals described herein may be represented using any ofa variety of different technologies and techniques. For example, data,instructions, commands, information, signals, bits, symbols, and chipsthat may be referenced throughout the above description may berepresented by voltages, currents, electromagnetic waves, magneticfields or particles, optical fields or particles, or any combinationthereof.

Techniques described herein may be used for various wirelesscommunications systems, which may include multiple access cellularcommunication systems, and which may employ code division multipleaccess (CDMA), time division multiple access (TDMA), frequency divisionmultiple access (FDMA), orthogonal frequency division multiple access(OFDMA), or single carrier frequency division multiple access (SC-FDMA),or any a combination of such techniques. Some of these techniques havebeen adopted in or relate to standardized wireless communicationprotocols by organizations such as Third Generation Partnership Project(3GPP), Third Generation Partnership Project 2 (3GPP2) and IEEE. Thesewireless standards include Ultra Mobile Broadband (UMB), UniversalMobile Telecommunications System (UMTS), Long Term Evolution (LTE),LTE-Advanced (LTE-A), LTE-A Pro, New Radio (NR), IEEE 802.11 (WiFi), andIEEE 802.16 (WiMAX), among others.

The terms “5G” or “5G communications system” may refer to systems thatoperate according to standardized protocols developed or discussedafter, for example, LTE Releases 13 or 14 or WiMAX 802.16e-2005 by theirrespective sponsoring organizations. The features described herein maybe employed in systems configured according to other generations ofwireless communication systems, including those configured according tothe standards described above.

The various illustrative blocks and modules described in connection withthe disclosure herein may be implemented or performed with ageneral-purpose processor, a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), or other programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Ageneral-purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices (e.g., a combinationof a DSP and a microprocessor, multiple microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration).

The functions described herein may be implemented in hardware, softwareexecuted by a processor, firmware, or any combination thereof. Ifimplemented in software executed by a processor, the functions may bestored on or transmitted over as one or more instructions or code on acomputer-readable medium. Computer-readable media includes bothnon-transitory computer storage media and communication media includingany medium that facilitates transfer of a computer program from oneplace to another. A non-transitory storage medium may be any availablemedium that can be accessed by a general purpose or special purposecomputer. By way of example, and not limitation, non-transitorycomputer-readable media can comprise RAM, ROM, electrically erasableprogrammable read only memory (EEPROM), or optical disk storage,magnetic disk storage or other magnetic storage devices, or any othernon-transitory medium that can be used to carry or store desired programcode means in the form of instructions or data structures and that canbe accessed by a general-purpose or special-purpose computer, or ageneral-purpose or special-purpose processor.

Also, any connection is properly termed a computer-readable medium. Forexample, if the software is transmitted from a website, server, or otherremote source using a coaxial cable, fiber optic cable, twisted pair,digital subscriber line (DSL), or wireless technologies such asinfrared, radio, and microwave, then the coaxial cable, fiber opticcable, twisted pair, DSL, or wireless technologies such as infrared,radio, and microwave are included in the definition of medium.Combinations of the above are also included within the scope ofcomputer-readable media.

Other examples and implementations are within the scope of thedisclosure and appended claims. For example, due to the nature ofsoftware, functions described above can be implemented using softwareexecuted by a processor, hardware, firmware, hardwiring, or combinationsof any of these. Features implementing functions may also be physicallylocated at various positions, including being distributed such thatportions of functions are implemented at different physical locations.

Also, as used herein, including in the claims, “or” as used in a list ofitems (for example, a list of items prefaced by a phrase such as “atleast one of” or “one or more of”) indicates an inclusive list suchthat, for example, a list of at least one of A, B, or C means A or B orC or AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein,the phrase “based on” shall not be construed as a reference to a closedset of conditions. For example, an exemplary step that is described as“based on condition A” may be based on both a condition A and acondition B without departing from the scope of the present disclosure.In other words, as used herein, the phrase “based on” shall be construedin the same manner as the phrase “based at least in part on.”

From the foregoing it will be appreciated that, although specificexamples have been described herein for purposes of illustration,various modifications may be made while remaining with the scope of theclaimed technology. The description herein is provided to enable aperson skilled in the art to make or use the disclosure. Variousmodifications to the disclosure will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other variations without departing from the scope of thedisclosure. Thus, the disclosure is not limited to the examples anddesigns described herein, but is to be accorded the broadest scopeconsistent with the principles and novel features disclosed herein.

1. A method comprising: providing, to a recurrent neural network (RNN),a first amplified signal associated with a first frequency and a secondamplified signal associated with a second frequency; providing the firstamplified signal to a first antenna of a plurality of antennas and thesecond amplified signal to a second antenna of the plurality ofantennas; mixing, at the RNN, the first and second amplified signals asinput data using a plurality of coefficients and an additional pluralityof coefficients, wherein mixing the input data comprises: calculatingfirst processing results based on the input data and signaling that isbased on respective outputs of a first layer ofmultiplication/accumulation processing units (MAC units) with theplurality of coefficients; and calculating output data based on thefirst processing results and signaling that is based on respectiveoutputs of a respective additional layer of MAC units with theadditional plurality of coefficients; providing, from the RNN, outputdata as a plurality of adjustment signals, the output data configured topartially compensate a harmonic frequency of the first frequency thatinterferes with the second frequency.
 2. The method of claim 1, furthercomprising: adjusting a first received signal and a second receivedsignal received at respective antennas of the plurality of antennas witha corresponding adjustment signal of the plurality of adjustment signal.3. The method of claim 3, wherein adjusting the first received signaland the second received signal received at respective antennas of theplurality of antennas with the corresponding adjustment signalcomprises: subtracting the corresponding adjustment signal from thefirst received signal and the second received signal to generaterespective compensated received signals.
 4. The method of claim 1,further comprising: compensating a first received signal received at athird antenna of the plurality of antennas with the first adjustmentsignal, the third antenna associated with the second frequency.
 5. Themethod of claim 1, further comprising: amplifying a first transmitsignal associated with the first frequency to generate the firstamplified signal comprising first amplifier noise associated with thefirst frequency; and amplifying a second associated with the secondfrequency to generate the second amplified signal comprising secondamplifier noise associated with the second frequency.
 6. The method ofclaim 5, wherein the harmonic frequency of the first frequency thatinterferes with the second frequency being generated when the firstamplified signal is amplified as part of the first amplifier noise. 7.The method of claim 5, wherein the harmonic frequency of the firstfrequency that interferes with the second frequency is a second-orderharmonic component of the first frequency.
 8. The method of claim 5,wherein the first frequency corresponds to 1.8 GHz in the 4G Long-TermEvolution (LTE) band, and wherein the second frequency corresponds to3.5 GHz in the 5G New Radio (NR) band.
 9. The method of claim 5, furthercomprising: transmitting, to a data center, the first and secondamplified signals, wherein the plurality of signals include dataindicative of at least one of a livestock status, a water usage status,an agricultural field status, a wind measurement, a power generationstatus, an oil flow status, an energy storage status, or a powerconsumption status.
 10. The method of claim 5, further comprising:generating an interfering difference frequency based partly oncomponents of the first amplifier noise and the second amplifier noise,the interfering difference frequency being generated based on a harmoniccomponent of the first frequency interfering with a harmonic componentat the second frequency.
 11. The method of claim 10, wherein theinterfering difference frequency is a difference of the second frequencyfrom the first frequency.
 12. The method of claim 11, furthercomprising: providing a second adjustment signal of the plurality ofadjustment signals based on at least the interfering differencefrequency; and compensating a second received signal received at afourth antenna of the plurality of antennas with the second adjustmentsignal, the fourth antenna associated with the first frequency.
 13. Themethod of claim 1, wherein a number of the additional layers of MACunits is associated with a number of the plurality of antennas.
 14. Anapparatus comprising: a plurality of antennas including a first antennaconfigured to transmit at a first frequency; a plurality of wirelesstransceivers configured to transmit signals and receive signals toantennas of the plurality of antennas; and a plurality of layers ofmultiplication/accumulation units (MAC units), including: a first layerconfigured to mix a plurality of signals as input data and delayedversions of respective outputs of the first layer of MAC units using aplurality of coefficients to generate first intermediate processingresults; and additional layers of MAC units of the plurality of layersof MAC units, each additional layer of MAC units configured to mix thefirst intermediate processing results and delayed versions of respectiveoutputs of the respective additional layer of MAC units using additionalcoefficients of the plurality of coefficients to generate secondintermediate processing results, wherein a recurrent neural network(RNN) is configured to provide a plurality of adjusted signals as outputdata, the output data based partly on the second intermediate processingresults.
 15. The apparatus of claim 14, further comprising: a pluralityof memory look-up units (MLUs) configured to store and providerespective the first or second intermediate processing results, whereina portion of the plurality of memory look up units configured to provideoutput data according to the input data being mixed using the pluralityof coefficients.
 16. The apparatus of claim 15, further comprising: aplurality of delay units configured to provide the delayed versions ofthe respective outputs of the first layer of MAC units based on thefirst or second intermediate processing results provided by respectiveMLUs of the plurality of MLUs.
 17. The apparatus of claim 14, whereinthe RNN is configured for compensation of an interfering harmonicfrequency of the first frequency.
 18. The apparatus of claim 14, whereinthe plurality of antennas further includes a second antenna configuredto transmit at a second frequency, and wherein the interfering harmonicfrequency of the first frequency is based on a difference of the secondfrequency and the first frequency.
 19. An apparatus comprising: aplurality of power amplifiers configured to amplify signals asrespective amplified signals; a plurality of antennas, each antennaconfigured to transmit a respective amplified signal; and a recurrentneural network (RNN) coupled to each antenna of the plurality ofantennas and configured to generate a respective adjustment signal of aplurality of adjustment signals based on the respective amplifiedsignals.
 20. The apparatus of claim 19, wherein the RNN comprising: aplurality of processing units configured for bit manipulation andconfigured to receive the respective amplified signal; and a pluralityof multiplication/accumulation (MAC) units, each MAC unit configured togenerate a plurality of noise processing results based on the respectiveamplified signal and a delayed version of at least one of the pluralityof noise processing results, each MAC unit further configured togenerate the respective adjustment signal of the plurality of adjustmentsignals based on each noise processing result.
 21. The apparatus ofclaim 20, further comprising: a plurality of memory look-up units (MLUs)configured to store and provide respective noise processing results,wherein a portion of the plurality of the MLUs configured to provideoutput data based on the respective amplified signals being mixed usinga plurality of coefficients.
 22. The apparatus of claim 21, furthercomprising: a plurality of delay units, each delay unit associated witha respective MAC unit and configured to provide the delayed versions ofrespective outputs of the plurality of MAC units based on a portion ofthe respective noise processing results provided by respective MLUs ofthe plurality of MLUs.
 23. The apparatus of claim 7, wherein a firstpower amplifier of the plurality of power amplifiers is configured toamplify a first signal associated with a first frequency and to generatea first amplified signal comprising first amplifier noise associatedwith the first frequency, and wherein a second power amplifier of theplurality of power amplifiers is configured to amplify a second signalassociated with a second frequency and to generate a second amplifiedsignal comprising second amplifier noise associated with the secondfrequency.
 24. The apparatus of claim 19, wherein the apparatusimplements an Internet-of-Things (IoT) smart home device, an IoTagricultural device or an IoT industrial device.